karen h. l. tso-sutter, leandro balby marinho* and lars schmidt-thieme information systems and...
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Karen H. L. Tso-Sutter, Leandro Balby Marinho* and Lars Schmidt-Thieme
Information Systems and Machine Learning Lab (ISMLL)University of Hildesheim
Samelsonplatz 1, 31141 Hildesheim, Germany
Tag-aware Recommender Systems by Fusion of Collaborative Filtering
Algorithms (Cited by 5)
in Proceedings of 23rd Annual ACM Symposium on Applied Computing (SAC'08), Fortaleza, Brazil.
Presented by Jun-Ming Chen 2/27/2009
2
Outline
Introduction Collaborative Filtering
Extension with Tags Fusing User-based and Item-based
Experiments Conclusions
3
Introduction
Tag (Action : tagging) enabling people to easily add metadata to content Improve search mechanisms
Collaborative tagging system folksonomies Web services
flickr, del.icio.us
Advantage better structure the data for browsing provide personalized recommendations fitting the users’ interests
4
Introduction
Recommender Systems (RS) aim at predicting items or ratings (preference score, 1-5) of items that the user are interested in.
To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge.
(attribute aware) RS algorithms is typically attached to the items and is usually provided by domain experts
an item always has the same attributes among all users
5
Introduction
tags are provided by various users not only associated to the items but also to the users act as additional background knowledge to improve RS algorithms
With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms.
6
Introduction
In this paper, we propose To integrate tags in recommender systems
by first extending the user-item matrix and An adapted fusion mechanism to capture the 3-dimensional correlations
between users, items and tags
Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results
7
Collaborative Filtering Recommender systems (RS) predict ratings of items or suggest a
list of items that is unknown to the user Two di erent recommendation tasksff
predicting the ratings, i.e. how much a given user will like a particular item predicting the items, i.e. which N items a user will rate, buy or visit next (topN)
Most recommender systems derive recommendations to a user by using opinions from people who have alike tastes, called neighborhood, while concealing the real identity of the users neighborhood.
The prevalent method in practice is Collaborative Filtering (CF)
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Collaborative Filtering Its idea is basically the nearest neighbor method
Given some user profiles, it predicts whether a user might be interested in a certain item,
based on a section of other users or items in the database.
There are in general two types of collaborative filtering: user-based item-based
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User-Based
Item-Based
Cosine
Probability
clustercustomers
weightitems
Recommondationweighted ordered
User-Based CF - Existing issues Low Performance when with millions of users and items.(user-
based filtering--KNN)
Low Prediction accuracy when with sparsity of data
2008-9-25 10
User-Based CF - Solution
Explore item-based collaborative filtering
Method: Build user-item matrix identify relationships between different
items use relationships to make recommendations
In contrast, item-based collaborative filtering analyzes historical transaction information to identify relations between items that
are co-purchased, uses these relations to compute the similarities between items, and make
recommendations based on item similarity.
2008-9-25 11
User-Based CF - Solution
It constructs a primitive model with significant amount of time, but makes recommendations in a very short time.
It then alleviates the problems of scalability and real- time performance that user-based CF faces.
2008-9-25 12
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Collaborative Filtering Notations
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Collaborative Filtering In user-based CF [19], recommendations are generated by
considering solely the ratings of users on items, by computing the pairwise similarities between users,
e.g., by means of vector similarity:
[19] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Analysis of recommendation algorithms fore-commerce. In Proceedings of the Second ACM Conference on Electronic Commerce (EC’00), pages 285–295, 2000. (Cited by 634)
w(u,v)
Useru
Userv
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Collaborative Filtering - recommendations
In user-based CF, to derive the recommendations for a target user u, usually only similarities of the k most-similar users are selected
(neighborhood – Nu).
When predicting a rating of a given user u for an item i, the weighted sum of the other users are computed by:
16
Collaborative Filtering A dualistic form of user-based CF is item-based CF [9], where
similarities are computed between each pair of items .
[9] M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions onInformation Systems. Springer-Verlag, 22/1, 2004. (Cited by 168)
w(i,j)
17
In the case of item-based CF, the prediction would be the average of the ratings of k most-similar items Ni
rated by the given user u.
In similar notation as user-based CF, the prediction for a rating of a given user u for an item i
Collaborative Filtering - recommendations
u
18
Extension with Tags < item, attribute > < user, item, tag >
We cope with this three dimensionalities by projecting it as three two-dimensional problem, < user, tag > < item, tag > < user, item >
19
Extension with Tags User tags, are tags that user u,
uses to tag items and are viewed as items in the user-item matrix.
Item tags, are tags that describedescribe an item, i, by users and play the role of users in the user-item matrix
20
Extension with Tags Let:
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Extension with Tags To apply user- and item-based CF after the extension with tags, both
CF algorithms have to be recomputed with the newly extended user-item matrix.
For user-based CF, the new user-item matrix , Ruextend := R + RTu, is represented in a U × Iextend matrix
For item-based CF, the new user-item matrix, Riextend := R + RTi , is represented in a Uextend × I matrix
Ruextend := R + RTu
U × Iextend matrix
Set : Users x (Items + user Tags)
Set : (Users + Item tags) x Items
22
Fusing User-based and Item-based Tags of an item, are descriptions of the item by one or more than
one users. Thus, tags are not only attached to the item itself but also are depended on the user’s preference.
This suggests that a RS algorithm that is able to capture both user’s and item’s aspect of tags would eventually be a suitable
choice.
We have selected an existing algorithm developed by Wang et al. [21], which fuses the predictions of user- and item-based CF.
[21] J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborativefiltering approaches by similarity fusion. In SIGIR ’06: Proceedings of the 29th annual international ACMSIGIR conference on Research and development in information retrieval, pages 501–508, New York, NY,USA, 2006. ACM Press. (Cited by 49)
23
Fusing User-based and Item-based The fusion of the user- and item-based predictions was done by
computing the sum of the two conditional probabilities that are based on user- and item-based similarities,
which are computed using standard user- and item-based CF. A parameter, λ, is introduced to adjust the significance of the two predictions.
j
λ = 0.4
24
Fusion for Predicting Item Problem Most systems that use collaborative tagging do not contain rating
information, i.e. only the occurrence, Ou,i {0,1}∈
Yet, the fusion method by Wang et al. [21] does not consider tags. not the predicting item problem
Thus, we propose a fusion algorithm that tackles the predicting item problem and also takes tags into account.
[21] J. Wang, A. P. de Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborativefiltering approaches by similarity fusion. In SIGIR ’06: Proceedings of the 29th annual international ACMSIGIR conference on Research and development in information retrieval, pages 501–508, New York, NY,USA, 2006. ACM Press.
25
Fusion for Predicting Item Problem For the predicting item problem in user-based CF,
recommendations are a list of items that is ranked by decreasing frequency of occurrence in the ratings of his/her neighbors.
For item-based CF, the topN recommendation suggested by [9] is to compute a list of items that is ranked by decreasing sum of the similarities of neighboring items, Ni, which have been rated by user u.
user-based being the frequency of items, and item-based the similarity of items
[9] M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions onInformation Systems. Springer-Verlag, 22/1, 2004. (Cited by 168)
26
Fusion for Predicting Item Problem To address the di erent meanings of the values, we normalized the ff
prediction lists to unity
The topN combined prediction list is thus:
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Fusion for Predicting Item Problem We deem that this tag-fusion algorithm is a suitable tag-aware RS
algorithm because user tags and item tags provide extra indications of the user’s and item’s
preferences, our adapted fusion approach then brings about both user and item
aspects of the tags concurrently.
In fact, our empirical analysis has shown that our tag-fusion RS approach provides promising results.
28
Experiments July 2006 by crawling the Last.fm site
1853 items (artists), 2917 users and 2045 tags users preference information
songs the users listen to tags information
user-end tagging of artists, albums, and tracks of the music
Appear at least 10 times in the tag assignments, i.e. (user, resource, tag) triples
Last.fm is a popular internet radio and music community website which allows user to tag the music.
29
Experiments 10-fold cross validation
split the training data to validation data to optimize the parameters λ(0.4) and k (20), the neighborhood size
focuses on the item prediction problem, which is to predict a fixed number of topN recommendations
(N = 10)
Suitable evaluation metrics are Precision, Recall and F1
Last.fm is a popular internet radio and music community website which allows user to tag the music.
30
Experiments
It can be seen that the fusion method, both with and without tags, significantly outperform the standard CF models.
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Experiments
It is interesting to see that incorporating tags to the baseline models does not improve the recommendation quality at all, in contrast to the promising results of including tags in the fusion method.
32
Experiments
Applying user/item tags alone does not exploit the characteristic of tags correctly. Hence, attaching tags to standard CF algorithms, does not improve the
performance at all, the tags are then only seen as noise.
By simply extending the standard CF algorithms with tags, it fails to denote the 3-dimensional correlations between user, item and tag, whereas the proposed fusion method has shown to be able to capture
this relationship.
33
Conclusions We have presented a generic method to include tags to standard
CF algorithms such as user- and item-based CF.
We have found an approach that deals with the 3-dimensional correlation between the users, items and tags by first applying our tag extension mechanism and then an fusion method which we have adapted from a predicting rating
problem to predicting item problem.
Our empirical analysis has shown that the proposed adapted fusion method outperforms standard baseline models, especially with the incorporation of tags.
Moreover, our findings have suggested that our adapted fusion method has successfully captured the relationships between users, items and tags.
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Presented by Jun-Ming Chen (Jacky)
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When George was about six years old, he was made the wealthy master of a hatchet of which, like most little boys, he was extremely fond. He went about chopping everything that came his way.
One day, as he wandered about the garden amusing himself by hacking his mother's pea sticks, he found a beautiful, young English cherry tree, of which his father was most proud. He tried the edge of his hatchet on the trunk of the tree and barked it so that it died.
Some time after this, his father discovered what had happened to his favorite tree. He came into the house in great anger, and demanded to know who the mischievous person was who had cut away the bark. Nobody could tell him anything about it.
Just then George, with his little hatchet, came into the room.
"George,'' said his father, "do you know who has killed my beautiful little cherry tree yonder in the garden? I would not have taken five guineas for it!''
This was a hard question to answer, and for a moment George was staggered by it, but quickly recovering himself he cried:
"I cannot tell a lie, father, you know I cannot tell a lie! I did cut it with my little hatchet.''
The anger died out of his father's face, and taking the boy tenderly in his arms, he said:
"My son, that you should not be afraid to tell the truth is more to me than a thousand trees! Yes - though they were blossomed with silver and had leaves of the purest gold!''
When George was about six years old, he was made the wealthy master of a hatchet of which, like most little boys, he was extremely fond. He went about chopping everything that came his way.
One day, as he wandered about the garden amusing himself by hacking his mother's pea sticks, he found a beautiful, young English cherry tree, of which his father was most proud. He tried the edge of his hatchet on the trunk of the tree and barked it so that it died.
Some time after this, his father discovered what had happened to his favorite tree. He came into the house in great anger, and demanded to know who the mischievous person was who had cut away the bark. Nobody could tell him anything about it.
Just then George, with his little hatchet, came into the room.
"George,'' said his father, "do you know who has killed my beautiful little cherry tree yonder in the garden? I would not have taken five guineas for it!''
This was a hard question to answer, and for a moment George was staggered by it, but quickly recovering himself he cried:
"I cannot tell a lie, father, you know I cannot tell a lie! I did cut it with my little hatchet.''
The anger died out of his father's face, and taking the boy tenderly in his arms, he said:
"My son, that you should not be afraid to tell the truth is more to me than a thousand trees! Yes - though they were blossomed with silver and had leaves of the purest gold!''
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