Group Recommendation Systems : A Survey

Nihal JainIndian Institute of Technology - Guwahati

154101051 (MTech , CSE)nihal.jain@iitg.ernet.in

Kavish N. DahekarIndian Institute of Technology - Guwahati

154101035 (MTech , CSE)kavish@iitg.ernet.in

ABSTRACTIn the recent years, group recommender systems have be-come more and more relevant. In essence, recommendationsystems provide crucial predictions of items that may in-terest individual users, based on previously collected data.Such recommender systems cannot cater to the current us-age scenario owing to the huge growth of social commu-nities, e-commerce platforms and availability of enormoususer-data in general. Group recommendation systems areextensions of such recommender systems, in that they seekto predict recommendations for a group of users rather thantargeting single users. This survey aims to analyze popularapproaches developed over the years to optimize and fur-ther improve this recommendation process. Most of thesesystems assume that group of users follow deterministic pat-terns of choices and decisions which can be algorithmicallystudied to classify separate users into individual groups.Basic techniques include aggregating individual user datainto groups using collaborative filtering which is further en-hanced by using genetic algorithms along with special heuris-tics that can predict interactions among users in a group.However, such predictions may dissatisfy few individuals inthe group. This survey also studies some novel methods thatremove irrelevant items and seek to improve overall groupas well individual satisfaction. Applications for these tech-niques range from content suggestions, friend recommenda-tions and even targeted advertisements.

KeywordsGroup recommendation, Collaborative filtering

1. INTRODUCTIONWith the ever increasing amount of products available aroundus, it is really difficult for a user to choose just one itemamong such an overwhelming list of possible choices. Rec-ommender systems help users in making good decisions byproviding them a list of items most relevant to them. Thus,a recommender system is a tool which aims to predict theitems a user may like based on his past behaviour.

A simple recommendation problem involves ‘n’ users and ‘m’items. A user u might have seen(used) ‘k’ items, say i1, i2, .. ik out of ‘m’ possible items. The goal of recommendationsystem is to predict rating for the ‘n - k’ remaining items,which have not been rated by the user. Various algorithmshave been developed over the years which make use of theavailable user data in some manner to make efficient recom-mendations. Collaborative filtering is one of the most widely

used and successful algorithm used to solve this problem.

Most of the initial research in the field of recommender sys-tems dealt mainly with the recommendation for individualsonly. Individual recommendations work quite well for usecases like news recommender systems where people seldomread an article together. But, in uses cases like restaurantrecommendation problem it was felt that there is a need tomake recommendations by also taking into account a groupof users having some similar taste, as normally people enjoydinners in a group.

PolyLens [2] was one of the earliest implementation of grouprecommender systems. It was designed in such a mannerthat the system provided the users privilege to form newgroups, see recommendation generated by the system basedon their groups’ taste along with their individual taste. Userswere also allowed to leave a group whenever they felt like do-ing so. PolyLens introduced the idea of using collaborativefiltering method to predict items that may be of use to groupof users. Earlier, people used collaborative filtering for in-dividual recommendations only. They also studied variousdesign issues related to the group recommendation problem.Basically, they created groups of users explicitly and maderecommendations for groups.

After the success of PolyLens, people realised that it is notappropriate to assume that a group of users always existin the system. So [1] developed the idea of automaticallydetecting groups of users. At first, Cosine similarity metricwas used to generate user similarity network. Then weightedmodularity optimization technique was used to classify usersinto groups. Next, The system generated predictions for allthe item rated by every member of the group. The groupprediction for remaining items were populated by first find-ing a list of top similar items of the unrated item. Then,average of all the top similar items were combined with theratings, which became the group rating.

Previous group recommenders process ratings given to avail-able items by individual users of the group. Though suc-cessful in the past, these recommenders fall short. Moreadvances were made in the field of group recommendationby including not just the user’s ratings for items but also in-cluding collective user ratings for items given by users as agroup. Such ratings displayed useful properties of the groupsuch as the interaction among the members, the dominantand influential group members etc. It was experimentally

shown that coupling these group ratings along with geneticalgorithms substantially improved the accuracy of the rec-ommendation predictions.

The recent decade has witnessed an explosion of technol-ogy, mainly the internet. Internet has now penetrated everynook and corner of human civilization, especially our socialcircles. More than a billion people on earth have a facebookaccount and over 70 percent of them are active. Onlinerecommendation systems can include various functionalitiesform helping users to find interesting music, books, moviesand even guiding their online purchases. Previous attemptsat group recommendation systems use various techniquesbased on collaborative filtering to make appropriate recom-mendations to individual as well groups is users. These te-chiniques are however tailored at a very low level to be effec-tive for offline environments. Also, aggregating and mergingthe preferences of all users of the group and serving recom-mendations based on these aggregations often leaves a fewusers of the group with unsatisfactory results.

2. POLYLENSPolyLens used the collaborative filtering algorithm to gen-erate group recommendations. This paper [1] mainly dealtwith finding answers to some unsolved questions regard-ing the group recommendation process. Their primary goalwas to implement such a group recommender system fromscratch and also to understand the group recommendationproblem in a better way. PolyLens had mechanism to createnew groups and manage them. Users could choose betweenindividual and group recommendations, whichever they foundmore useful. Users were notified whenever someone theyknow invited them to join a group. The designers of PolyLensidentified some primary design issues of group recommendersystems.

2.1 Nature of a groupA group can be either persistent, if users agree to get rec-ommendations for the same group of members over a periodof time, or ephemeral, if groups are formed to get grouprecommendations for only once.

Based on privacy issues a group can be either private orpublic. A group is said to be private if the existence of thegroup is known to only the members of the group. Onlymembers have the right to access group recommendationsfor that group. On the other hand a group which is visibleto the whole world and thus accessible to all it is said tobe a public group. PolyLens made use of persistent privategroups.

2.2 Group creationThe number of groups that might get created and their na-ture depends on the creator of the group. An administratoris the person who is responsible for group creation. He de-cides the nature of such groups. The designers’ of PolyLensgave each user the right to create a group. But, only thegroup creator could add new members to the group via invi-tations. Thus, though being inherently involved in the groupformation process, normal users are devoid of certain func-tions which only certain users can perform, namely addingmembers to a group created by the user.

2.3 User control over his dataIn order to make intelligent recommendations, every systemneeds to log some amount of user data. Generally, a userhas two different types of data:

• ratings, which is the score that a user gives to an itemand

• predictions, a list of items recommended to the userbased on his data.

Ratings are the personal data while predictions are machinegenerated data. A user decides if he should make his datavisible to other users. In order to make group recommen-dations, it is important that the user permits the system toaccess his data, which in turn is used to make group predic-tions. PolyLens provided its users the power to accept orreject a group invitation.

2.4 Making recommendations for groupsThe recommendation process basically had two fundamentalsteps:

• Choosing social values function which defines the roleof an individual user in group recommendation com-putation.

• Implementing the algorithm so that it efficiently pro-duces recommendations based on the chosen social valuefunction.

Group recommendations can be computed by assuming thata non-existent user, say user G exists in the system. Here,G’s taste represents the overall taste of the group. Now, wecan produce recommendation for this user G by aggregatingthe ratings of all the members in the group. Thus, what-ever is recommended to G becomes recommendation for allthe members of the group. PolyLens computes group rec-ommendation by first generating recommendations for eachmember of a group. Finally, they merge the list using theprinciple of least misery.

3. GROUP DETECTION ALGORITHMEarlier methods assumed that a group of users having simi-lar taste already exist in the system. But, it is possible thatthe existing system might be implemented in such a man-ner that it does not divide the users into groups. In sucha situation, we need some mechanism to identify a groupof similar users. This paper [1] studied how to automat-ically detect groups of similar users. After identifying allthe possible groups, they implemented an algorithm whichgenerates group recommendations. The group detection al-gorithm had four key steps. They found out that the qualityof the results is directly proportional to the number of groupscreated by the algorithm.

3.1 Finding the similarity of usersThe algorithm starts by taking rating matrix, which con-tains a column for each user and the ratings given by thatparticular user to each item along the rows, as input. Then,

the standard cosine similarity metric was used to find simi-larity among any two users, say u and v. The equation is asfollows:

simuv = cos(u, v) =uv

‖u‖‖v‖

Similarity value was computed for every possible pair ofusers. The output of this step was a similarity network,where each node represents a particular user and a weightededge represents how similar the two adjacent nodes i.e., userswere. The edges of the similarity network suffered fromnoise, which lead to ambiguities in group detection. Con-sidering the example of movie recommender system, theremay exist a critic who rates a movie which he likes as 5,while another critic who rates a movie he likes as just 4.This example demonstrates that different people may havedifferent rating scales. To deal with this, a parameter callednoise was used. This noise was subtracted from every edge.

3.2 Detecting groupsIn order to detect communities, a group detection algorithmwas used which took the user similarity network as input.This method is based on a heuristic method of weightedmodularity optimization. It can handle networks havingeven millions of nodes efficiently. Essentially it creates a treewhich hierarchically divides users into groups of increasinglevels, as output. Thus, the algorithm classified the usersinto groups use by the group recommendation algorithm.

3.3 Predicting ratings for items not rated byenough group members

The algorithm then computed ratings that a group of usersmay give to an item such that every such item was rated byat least co-ratings percent members. Co-rating is a parame-ter which represents the minimum percentage of group mem-bers who have rated an item. For each item, the arithmeticmean of the ratings of all the group members is calculated.

Hence, for each item i, the corresponding rating r is givenby the following equation:

ri =1

n

n∑u=0

ru

where n is the number of members in the group who hadearlier rated item i and ru is the rating given by user u tothat item. The calculated value becomes the group ratingfor that item.

3.4 Predicting ratings for rest of the itemsIn the previous step, it was not possible to predict rating forall those items which do not satisfy the minimum percentagecondition. For all such items which failed this test, itemsimilarity for each item pair was computed. The similaritybetween item i and item j, denoted by, sij can be measuredwith help of the cosine similarity metric. The similaritieswere stored in a network called items similarity network.

For each item which was not rated by the group, a list oftop k similar items w.r.t. the item under consideration wasproduced. The rating for that unrated item was predictedby making use of its top similar item list along with the

rating. Thus, the group rating for item i, denoted by ri isgiven by:

ri =

∑kj=0 rj .tij∑kj=0 tij

where k is the number of items in the top similar list.

In order to make reliable predictions a trust parameter wasused. It indicated the minimum value required by the meanof the top similar items in order to be considered reliable.

4. CONSIDERING INTERACTIONS AMONGGROUP MEMBERS

All previously proposed group recommenders consider rat-ings given to available items by individual users of the group.Such systems have been successful in the past, however, con-sidering the dawn of the internet age and the explosion inthe amount of networking and group activity that every av-erage internet user is involved in, these recommenders fallshort. The most important factor leading to the above con-clusion is the magnitude of social influence. Individuals ingroups are constantly being influenced by every action takenby other members of the group. Leadership as well as ma-jority of opinion also has major effect on how a user behavesand how his/her likes and disliked transition over a period oftime. A good recommendation system should take all theseinteractions[3] into account before making predictions andrecommendations for other users of the group. That’s whya group recommendation engine should thoroughly considerall integration within a group instead of simply aggregatingthe user ratings given by each user.

4.1 Current approach towards item ratingsPresently, collaborative filtering based group recommenderapproaches can be broadly classified into three types. Theyare enumerated below:

• Employing a pre-defined aggregation strategyThis approach is motivated by a pre-concieved notionabout the group and an aggregation function is pre-defined for every possible test-case that the group willbe a part of. In such an approach, all user ratingsfor specific items from a group of users is aggregatedover a weighted pre-defined function and the result-ing output is matched with user specified thresholdsin-prder to draw conclusions over which items will bechosen for recommendations. The drawback of suchan approach is that not all items are rated by everyuser. In worst cases, an item may not be rated byany user at all and in such cases the approach fails.Also, it is already established the fact that a good rec-ommendation system should thoroughly consider theinteractions between individual members of the group,which this method fails to follow.

• Gathering additional details from usersIn many scenarios, availability of meta-data apart fromabsolute item ratings has proven to improve the accu-racy of the results. In this case, users are asked forspecific information regarding their interactions withthe group and how exactly are their ratings relevantto the items they are interacting with. This additional

information is then used for generating more accu-rate recommendations for other members of the group.Though this method seems to follow up on the notionof considering group interactions along with user rat-ings, it involves an increased amount of time and effortwhich might not always be a viable option. Thus, thismethod too fails to meet the criteria for an improvedgroup recommendation system.

• Involving Domain expertsThis method involves using domain experts to monitorand channel the aggregation and combination processof the user ratings. The involvement of such expertiseleads to obvious disadvantages of increased time andeffort. However, dependence on such expertise meansthe integrity of the results entirely rest on the com-petency of the experts. Hence, this approach is alsolimited in its practicality. . . .

4.2 Proposed approach towards considering groupinteractions

It is assumed that data being supplied as input to the sys-tem will contain pre-ordered sets of item ratings which aremapped to individual members which are in turn mappedto individual groups. Following is a quick explanation of theterminologies used.

Let G be the group of users being studied.Let U be the set of all users in the system.So, U = u1, u2, u3, ..., un where ux is a user of the system.G ⊆ ULet I be the set of items being studied in the group.So, I = i1, i2, i3, ..., im where ix is a target item.The primary difference of this approach is that:

• It will not depend on additional information providedby users

• It will not depend on domain experts guiding the resultaggregation . . .

Previous researches made recommendation prediction solelybased on individual item ratings by users of the group. How-ever, this approach will also use ratings given to items collec-tively for a group. Such ratings can help identify interactionswithin the group and hence help improve the accuracy of theresults.For example, if G is a group of users u1, u2, u3 and let I bethe set of items i1, i2, i3, i4, i5. Say user u2 rated item i2as 5 and user u3 rated item i2 as 1. Also, user u2 and u3

collectively rated item i2 as 4.R(u2, i2) = 5R(u3, i2 = 1R(u2, u3, i2) = 4Direct conclusions about the behavior of the group can bemade from the above data as follows:

• User u2 clearly favors item i2 due to his high ratingfor the said item

• User u3 on the other hand does not highly favor itemi2• When looking at their collective rating however, we

see that they rate the item relatively higher than theaverage of what the two users have individually ratedthe item

• We can conclude from this information that u2 is aninfluential user of the group whose ratings are favoredby other users. Hence during aggregation, we will sup-ply higher weights to u2’s ratings in order to improvethe efficacy of the system.

Considering the previous section that details the currentapproaches being used, the above suggested system clearlyscores higher in terms of practicality as well as experimen-tal promise. It does not consider only single user ratings,nor does it expect users to provide additional informationand most of all, it does not rely on any specific users of thesystem to improve the accuracy of the recommendations.

4.3 Supporting studies being usedFollowing two works form the core basis for the functioningof this system and the current section aims to detail themin brief.

4.3.1 Collaborative FilteringPersonalized recommendation systems based on collabora-tive filtering[5] can be generally classified into the followingcategories:

• Collaborative RecommendationsCollaborative recommendations take into considera-tion the fact that individuals with similar tastes willthese similarities in many other scopes as well. Hence,if two persons are found to be of similar taste(belongingto the same group), if one of them is interested in itemI, it is highly likely that the other person will also beinterested in item I and hence item I can be suggestedas a recommendation to him/her. These techniquesare widely used and form the fundamental basis formany popular recommendation systems.

• Content-based recommendationsThese recommendations considers the similarities ofitems rather than those of users. Hence, if an individ-ual has been found to rate high on specific items, thenall similar items to that item can be said to be likelycandidates for recommendation. Such approaches arewidely used on social networks and e-commerce webportals.

• Hybrid approachesThese approaches combine the above two, namely col-laborative recommendations as well as content-basedrecommendation to provide highly user aware as wellas item aware recommendations to users. Such method-ologies eliminate many of the potholes discovered inprevious attempts at recommendation systems.

4.3.2 Genetic AlgorithmThe Genetic Algorithms is in fact a searching technique thatis based on natural laws and principles of genetic selectionand natural survivalism. Such methods are very effective,especially with use cases have a very large cardinality. Basi-cally, the genertic algorithm treats every piece of data as achromosome. Each chromosome will be composed of manygenes. The data needs to be encoded in this format inorderto be used by the genetic algorithm. The encoding function-ality tried to find an appropriate method for mapping inputdata to chromosome strings. A population in this context isdefined as a collection of arbitrary chromosomes. The stepscan be enumerated as follows:

• Encoding the data and mapping it to chromosomes

• Segregating the chromosomes into populations

• Fitness function evaluation (used for determining qual-ity of chromosome)

• Genetic operators

The genetic operators are used for recursively reproducingnext generations of chromosomes in such a way that eachchild chromosome will inherit the target attributes from itsparent. Two parent chromosomes can also combine to createa crossover offspring. A mutation operator will randomlymodify parts of a chromosome or its constituent gene toensure that no parent is cloned into its offspring.

4.4 Proposed methodologyConsider again the terminologies discussed in section 4.2:Let G be the group of users being studied.Let U be the set of all users in the system.So, U = u1, u2, u3, .., un where ux is a user of the system.Let I be the set of items being studied in the group.So, I = i1, i2, i3, ..., im where ix is a target item.G ⊆ U| G | is number of members in group G.For example, if group G is defined as G = u7, u12, u9, u2, u4

then | G |= 5.Input to the system will be data sets I,U and G.

Step 1: Calculate possible neighborhood of ipThe nearest neighbor technique is used in this step to cal-culate all possible neighbors of ip i.e all items similar to ip.The Pearson correlation is used for calculating this similar-ity between items. A pre-determined threshold is used forcalculating Let Iz be the set of all such items resultant fromstep 1.Step 2 : Filter possible neighbors from the approximatedneighborhood IzHere, for each item iz in Iz, we will do the following:

• Has the group G rated the item iz?

• If no, then apply the filter to determine whether theitem qualifies to be a neighbor of ip.

• If yes, add it to Iz. If no, then it cannot be a neighborof ip, discard it.

• For Iz, estimate the members’ ratings for each itemiz.in Iz.

Step 3: Predict the group’s rating by calculating a weightedaverage of all the ratings.Step 4: If the rating is above a certain pre-determined thresh-old then include the item in probable recommendation list.Step 5: Sort the list in decreasing order of weighted ratingaggregation and server them to the user as recommenda-tions.

4.5 Comparison of resultsExperimental data over large data sets such as 20 <= |G| <=100 have shown that the presence of ratings for items givenby users as a group along with individually rating the itemvastly improves the accuracy of the genetic algorithm. Re-sults show an average precision of 0.8 for such grouped ratingover the 0.7 observed for data sets with only individual itemratings.

5. GROUP RECOMMENDATION IN ONLINESYSTEMS

Large communities of users exist over the internet regularlyideas, exchanging data and most importantly querying formore data. A critical factor for the survival of such com-munitites is retention factor which can be improved only byincreasing the amount of quality data being shared in thecommunity and an online group recommender is preciselysupposed to work in this area. All previous individual andgroup recommendation systems were focused on offline sys-tems and hence a more suitable approach for online commu-nities is required. Typical collaborative filtering techniqueswere used for generating base recommendation sets for on-line groups of users[4]. A second filter pass was made overthis generated result set to remove all irrelevant results andimprove overall satisfaction of each h member of the group aswell as the group on whole. Thus, the GR O(Group recom-mender for Online Communities) works in two passes. Thefirst pass uses a profile based filtering method for generatingresults satisfactory to the overall group. This includes threesteps

• Group profile generation

• Neighbor group formation

• Top-n candidate set creation . . .

The second pass employs individual profile based filtering inorder to minimize the dissatisfaction of the individual groupmembers. This step consists of two steps:

• Relevance evaluation

• Final recommendation set generation . . .

5.1 Related workAs with almost every other technology in the field of grouprecommendation, this system banks heavily on collabora-tive filtering and other previously developed systems such asPolyLens (O’Connor et al., 2001), Pocket RestaurantFinder(McCarthy, 2002), Adaptive Radio (Chao, Balthrop, For-rest, 2004), TV4M (Yu, Zhou, Hao, Gu, 2006) and Flytrap(Crossen, Budzik, Hammond, 2002). This system does notblindly aggregate all the user’s ratings but instead uses thesecond phase to generate recommendations for each groupmember and then aggregate them into a final result set.

5.2 Group Recommendation ProcedureAs discussed before, the system works in two passes whichare described in detail below.

5.2.1 Pass I: group-based filteringIn the context of this system, a user-profile is simply a de-scription of the user’s interests and or preferences which willform the core input for the collaborative filtering recom-mender. All user profiles are merged into a group profile asfollows:If pci k = 1 - if user c belonging to group i is interested initem kpci k = 0 - otherwiseThe group profile of all members c = 1, 2, ..., C is the vectorp1Ik), p2Ik)... such that :

P ik =

∑Cc=1 p

xi k

Where k ranges from 1, 2, ..,K for each book.Once we have a group’s profile, we calculate the similaritiesbetween group profiles and find the nearest neighbors of thegroup we are targeting. This is done using a typical nearestneighbor algorithm.For any two given group profiles a and b, the similarity be-tween them will be denoted by s(a, b) which will be obtainedusing any one of the two methods of Pearson co-efficient orthe cosine measure. Previous studies have shown the Pear-son correlation to be stronger due to its immunity to ratioand interval scaling. Once we calculate the similarities be-tween all other groups and our target group, we select atop few of them based on a pre-defined threshold. Finally,we extract the top-n results from the neighborhood of thegroup which forms the result item sets that the target groupis most likely to be interested in.

5.2.2 Pass 2: Filtering based in individual profilesMany of the existing group recommenders stop after the firstphase and hence might leave some users with dis-satisfactoryresults. The second pass ensures that such results are prunedout to increase satisfaction level. This phase includes twosteps, first calculating the relevance between each memberuser and each item and then filtering these items based onthe relevance.For the first step we use the users’ feature based profilesas well as item profiles from the initial data set. An itemprofile will include data about the characteristics of theitem. The item profile for an item k is represented as avector(vk1 , v

k2 , ..., v

kn) where,

vil = 1 - if item i contains keyword lvil = 0 - otherwiseThe feature based user profile belonging to a user u is rep-resented as a vector qc1, q

c2, ..., q

cn where:

Qcl =

∑Kk=1 p

cIk ∗ vkl

In the second step we try to reduce the dissatisfaction ofmembers of the group by eliminating items from the resultrecommendation set using the compatibility scores calcu-lated above. Thus, the recommendation ends being biasedtowards active members thus increasing the accuracy of thepredictions and reducing overall dissatisfaction with the re-sults.

5.3 ObservationsStrict experiments performed on groups of users who werebeing recommended new books using the recommendationsystem developed showed that the second pass indeed im-proved overall credibility of the result set. Varying recom-mendation set sizes from 5 to 20 were used to show thatthe final result is being successfully modded to be biased to-wards more active users thus increasing the precision of theoutput. Precision metric was calculated as follows:

Precision =total selected items

total recommended items

Experiments showed that precision ranged from 0.40 to 0.63increasing as the size of the group decreased.

6. CONCLUSIONSThough a substantial amount of time and effort has beenspent in the field of effective group recommendation strate-gies, the area still heavily banks on traditional algorithmssuch as collaborative filtering and thus inherits most if notall the limitations of these approaches. These approachedwill mainly fail in situations where data regarding user in-teractions with the concerned items is not explicitly avail-able.More research is required in this field in order to re-move problems such as ambiguity of recommendations, falsepositives in neighborhood findings, etc. However, all themethodologies surveyed in this paper make incremental de-velopments on top of each other to provide quite satisfactoryresults in the end thus making group recommenders a verypractical, quite effective and highly applicable system.

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