Modeling Users' Intentions for the Enhancement of Music Recommender Systems

Asma RafiqPh.D. Student

Centre for Digital Musicasma.rafiq@eecs.qmul.ac.uk

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Content

• Introduction• Research Questions• Research Plan• Conclusion

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Introduction

• Problem: Online music stores offer millions of songs to choose from, users need assistance.

• Solution: Music Recommender Systems• Using social and expert’s tagging, ontologies

and the semantic web, this research work seeks to assist people in finding the music according to their mood.

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Motivation• The power of music to induce moods in listeners.• “People often say that they select music according

to their mood.”• “the connection between mood and music is

explicit in our culture where beloved tunes bear titles like ‘In the Mood’ and ‘Mood Indigo.’” - Carroll, N., 2003. “Art and Mood: Preliminary Notes and Conjectures” Monist 86 p. 545

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Recommender Systems• Recommender systems aim to support users in their decision-

making while interacting with large information spaces. • They recommend items of interest to users based on

preferences they have expressed, either explicitly or implicitly. • Recommender systems help overcome the information

overload problem by exposing users to the most interesting items, and by offering relevance.

• Recommender technology is hence the central piece of the information seeking puzzle. Major music services such as Last.fm are using recommendation technology in ubiquitous ways.

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Recommendation Strategies• Collaborative Filtering is a widely used approach to solve the

recommendation problem. The stored interaction (explicit or implicit) between the users of the system and the item set helps generate informed guesses for recommendations.

• Content-based Filtering collects the information regarding the items and based on user preferences filters the results that the user is most likely to prefer. It simply depends on item description rather than the user ratings.

• Context-based Filtering uses the contextual information to describe the items.

• Demographic Filtering draws results based on stereotypes of users that like certain item.

• Hybrid Filtering is simply the combination of two or more recommendation strategies.

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Music Recommender System• Music is inherently different than other types of media. The space of

recommended items is extremely large as compared to other domains .

• People interact with music differently than they do with other types of media e.g. repetitive listening.

• Listeners vary their music preference based upon context and activities.

• Listeners enjoy listening to sequences of songs often getting as much enjoyment from the song transitions as from the songs themselves.

• It is important to consider the special nature of music when building recommenders for music.

• The uniqueness of music as recommendation domain present challenges not seen in other recommender domains.

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Research Questions• What are the resources that can be used to model the intentions of

the users for recommendation strategies? – Profiles are very important resource to recommend music to the users, people

prefer the genres that their friends and family listen to.– Profiles of people outside of one’s own network might also play role in music

discovery as some users indicated in Music Valley application feedback.– Music listed in Social Network websites, might not reveal the taste of the user,

as many other factors might be influential on listed preferences e.g. band promotion requests by acquaintances, which might not actually be listened by the users.

– The users age is important in evaluating user music preferences, also the system restricts the profile to above 18, the users mostly lied in the range of 24-45 years of age.

– The education level for most of the users was post graduate. Indicating that the system was mostly used by highly educated people.

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– Profiles of users reveal important information such as demographic information, interests, event information, etc. that can be used to design recommender models

• What are the ethical implications involved in acquiring these resources?– The ethical implications were carefully analysed for the experiment

participants. – Subjects under the age of 18 should not be allowed access to the

system due to strict laws in various countries regarding underage teens and children.

– Some of the subjects when requested for granting the permissions for using the application denied the permissions which was required for the data extraction from their profiles.

– It seems some social network users have high privacy concerns.– Some users suggested to explain the application functionality first

before asking for the permissions. However, the links to privacy statement and terms of services were always ignored by the users.

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• What is the state of the current music recommender systems and how to improve it using the resources identified as useful in user-intention extraction?– Music recommender systems have limited access to user information

spread all over the web, linked information can help model user taste more accurately.

– Recently, some music recommender systems incorporated the importance of social aspect of music introducing features like playlist sharing, commenting on friend’s playlists, etc.

– Social network profiles are identified as useful resource for music recommendation. It could be used to creating a trust based music recommender system with the identified music taste leaders.

– Music recommender systems might improve results by adapting to the users current situation.

– The existing recommendation techniques such as content-based, collaborative filtering or hybrid techniques focus on users explicit contact behaviours but ignore the implicit relationship among users in the network.

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• Has semantic web reached the level of maturity to develop the anticipated sophisticated music recommendation systems?– To answer this question a detailed analysis of current semantic web is

required– I shall conduct this in the next month which is also required to design

the next phase– Knowledge base could be established that contains information as

ontologies

• How can we evaluate the success of user modelling in recommender system?– The evaluation of the system can be done by comparing the social

music recommender system and semantic music recommender system– The user’s ratings and feedback will be used for this purpose

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Research Plan

• Phase 1: Social Music Recommender Systems– The experiment will be finalised by the end of this month and results

and feedback on the experiment shall be helpful in drawing various conclusions and proposing future work.

• Research paper accepted in August 2011– Addressed research questions related to music discovery and

recommendation using online social networks– In this paper, I have drawn conclusions on the present literature

regarding social networks, web music communities, how such systems can be useful and proposed future work

– Camera-ready version due on 19 September 2011– I will be presenting this paper in WOMRAD in conjunction with ACM

RecSys 2011 on 23 October 2011 in Chicago, IL

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Facebook Application

• A social music recommender system is a system that extends on social interaction and events

• Identify user’s event posted on Facebook• Extraction of this information through the Facebook Graph

APIs

• Recommend music based on the preferences of participants of the event

• Suggest the most popular songs using Youtube recommendations

• Demo19/04/23

User Feedback

• The user was surprised to see her favorite songs being recommended to her, I ellaborated the experiment and she told me that the bands/artists she listed in her preferences (a total of 8) were not the bands she actually listened to, rather they were some friends whom she was asked to promote by ‘liking’ their fan page. This revealed that the recommendations generated from friends' preferences were leading to more useful results for this user.

• Saves time for a user to think of what she wants to listen next (as the songs are recommended as a playlist).

• The system has been successful in re-discoveries of songs by musician/bands that a user admired while she was a teen but had forgotten about.

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• The usual Youtube recommendations did not interest a user as she said: “I never liked the recommendations - it is always what Youtube want to sell me i.e. the songs by the song owners /publishers - who always have advertisements at the start of the clip. So, I never click on these recommendations but this is much better as a player because its playing the songs I know, and want to listen to and not the ones that will show ads first”.

• Sarcastic appreciation: “This system is creepy! I have been recommended a song from a band that I used to login to Facebook account!! I smell conspiracy theory here...”

• “The application asks for many permissions; an explanation before a user is asked to grant the permissions would be helpful to know what is going on...”

• “The next stage for music recommendation people could also pick the quality they like to listen to i.e., only choose clips that have HD sound or don't pick the ones that are kids playing in their bedroom!”

• The songs should be mixed within the playlist from different artists, it makes it boring to use the system for a randomly picked artist playlist.

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Problems with Social Platforms• Change of backend support without prior notice

– FBML and RestAPI are deprecated

• Change of interface without prior notice– The application is displayed in a canvas on the Facebook platform. The canvas

is actually an IFrame in HTML. An IFrame is a visual part of a Web page which contains another Web page. When the Facebook page of the application is rendered on the client, the IFrame interacts with our server to authenticate the user and the application.

– Now, the allocated space is also reduced which used to be of 760 pixels wide.– Fetching friends data using the Graph API is quite slow (takes up to 20

seconds for one user)– Even the display of Facebook users in the application is slow (getting their

name and picture). – All in all working with the Facebook API is problematic.

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Phase 2: Semantic Music Recommender System

• One good application is to illustrate the effect the semantic Web can have on music recommendations, once data has become semantically structured.

• We are going to look at music ratings as a semantically rich information source that when related to music files has many advantages and creates a whole new Web of potential applications.

• User-to-user similarity is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user similarity the ratings assigned by two users to a set of items are pairwise compared and averaged (correlation).

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• In this experiment we intend to make user-to-user similarity adaptive, i.e., we dynamically change the computation depending on the profiles of the compared users and the target item whose rating prediction is sought.

• We propose to base the similarity between two users on the subset of co-rated items which best describes the taste of the users with respect to the target item.

• These are the items which have the highest correlation with the target item.

• We will evaluate the proposed method to show that the proposed locally adaptive neighbor selection, via item selection, can significantly improve the recommendation accuracy compared to standard CF.

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• Semantic Music Recommender System is a personalised music recommender system which tries to limit the problems of collaborative recommender systems by ontologically using semantic information from the categorical characteristics of an item such as Genre.

• The similarities between user pairs will be calculated by a weighted mean method that calculates three similarity measures: – The similarity of user evaluation histories (using the Pearson

correlation coefficient on usage information of the system in terms of a user-item evaluation data);

– The similarity of these user's demographic data – The users similarity in interest or preference based on the semantic

similarities of the items retrieved and/or evaluated.

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• The use of ontologies in these types of systems limits specific problems, including the following:– To guarantee the inter-operability of system resources and the

homogeneity of the representation of information.– To allow for the dynamic contextualisation of user preferences in specific

domains.– To facilitate performance in social networks and collaborative filtering.– To improve communication processes between agents and between agents

and users.– To limit the "cold start" problem by completing the incomplete information

through inferences.– The ability to semantically extend descriptions of user contextual factors.– To improve the representation and description of different system

elements.– Improve the description of system's logic by admitting the inclusion of a

set of rules.– Provide the necessary means to generate descriptions enriched by web

services and facilitate their discovery by software agents.

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Dataset Candidates• Dataset 1: Yahoo! Music User Ratings of Musical Artists, version 1.0

– This dataset represents a snapshot of the Yahoo! Music community's preferences for various musical artists.

– The dataset contains over ten million ratings of musical artists given by Yahoo! Music users over the course of a one month period sometime prior to March 2004.

– Users are represented as meaningless anonymous numbers so that no identifying information is revealed.

– The dataset can be used by researchers to validate recommender systems or collaborative filtering algorithms.

• Dataset 2: Million Song Dataset– It is a freely-available collection of audio features and metadata for a million

contemporary popular music tracks.– The dataset does not include any audio, only the derived features.

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Phase 3: User Modeling in Music Recommender System

• In the long-run, I intend to design a framework that incorporates the best and possible integration of these two intention modelling perspectives (social and semantic) in order to model the intention of the user effectively.

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Gantt Chart of Future Plans

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Conclusion

• People need assistance to search the needle in the haystack in this era of information overload. – The best way to achieve this is to make our computing machines ‘aware’ of the user

intentions.

• The semantic and intentions gaps need to be addressed in order to achieve satisfactory results.

• It is the need of time to review the enhancements in the recommendation systems with resources available that could be extracted from current massive media to model the user intentions in order to improve the music experience for a wide range of people; leading to the development of a improved music recommendation system

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Questions and Comments

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