Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

Modeling Users for Adaptive Information Retrieval by Capturing User Intent

In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a user’s intent is captured and used for improving his/her effectiveness in an information seeking task. The user intent is captured by analyzing the commonality of the retrieved relevant documents. The effectiveness of our user model is evaluated with regards to retrieval performance using an evaluation methodology which allows us to compare with the existing approaches from the information retrieval community while assessing the new features offered by our user model. We compare our approach with the Ide dec-hi approach using term frequency inverted document frequency weighting which is considered to be the best traditional approach to relevance feedback. We use CRANFIELD, CACM and MEDLINE collections which are very popular collections from the information retrieval community to evaluate relevance feedback techniques. The results show that our approach performs better in the initial runs and works competitively with Ide dec-hi in the feedback runs. Additionally, we evaluate the effects of our user modeling approach with human analysts. The results show that our approach retrieves more relevant documents to a specific analyst compared to keyword-based information retrieval application called Verity Query Language.

Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling

The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of information resources. This chapter investigates methods for enabling improved navigation, user modeling, and personalization using collaboratively generated tags. The authors discuss the advantages and limitations of tags, and describe how relationships between tags can be used to discover latent structures that can automatically organize a collection of tags owned by a community. They give a hierarchical clustering algorithm for extracting latent structure and explain methods for determining tag specificity, then use visualization to examine latent structures. Finally the authors discuss future trends including using latent tag structures to create user models.

Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment

Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an information need. Recent advances in both Web technologies, such as the sociable Web of Web 2.0, and computer hardware, such as tabletop interface devices, have enabled multiple users to collaborate on many computer-related tasks. Due to these advances there is an increasing need to support two or more users searching together at the same time, in order to satisfy a shared information need, which we refer to as Synchronous Collaborative Information Retrieval. Synchronous Collaborative Information Retrieval (SCIR) represents a significant paradigmatic shift from traditional IR systems. In order to support an effective SCIR search, new techniques are required to coordinate users’ activities. In this chapter we explore the effectiveness of a sharing of knowledge policy on a collaborating group. Sharing of knowledge refers to the process of passing relevance information across users, if one user finds items of relevance to the search task then the group should benefit in the form of improved ranked lists returned to each searcher.In order to evaluate the proposed techniques the authors simulate two users searching together through an incremental feedback system. The simulation assumes that users decide on an initial query with which to begin the collaborative search and proceed through the search by providing relevance judgments to the system and receiving a new ranked list. In order to populate these simulations we extract data from the interaction logs of various experimental IR systems from previous Text REtrieval Conference (TREC) workshops.

A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval

Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding users to formulate clear requests based on their common nature of querying for information. The approach known as the front-end approach gives users an overview about the system data through a virtual data component which stores the extracted metadata of the data storage sources in the form of an ontology. This approach reduces the ambiguities in users’ requests at a very early stage and allows the query process to effectively perform in fulfilling users’ demands in a context-aware manner. Furthermore, the approach provides a powerful query engine, called context-aware querying, that recommends the appropriate query patterns according to the user’s querying context.

Collaborating Agents for Adaptation to Mobile Users

The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is based on a filtering process which considers both the user’s current context and her/his preferences for this context. The authors propose an object-based context representation, which takes into account the user’s physical and collaborative contexts, including elements related to collaboration tasks and group work in which the user is involved. They define the notion of preference for an individual or a group of people that develops a collaborative task and give a typology of preferences before proposing a formalism to represent them. This representation is exploited by a context matching algorithm in order to select only user preferences which can be applied according to the context of use. This chapter also presents the framework PUMAS which adopts a Multi-Agent System approach to support our propositions.

Computing Recommendations with Collaborative Filtering

Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.

COBRAS

In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference recommender system. COBRAS’s task is to find relevant documents and interesting people related to the interests and preferences of a single person belonging to a like-minded group in an implicit and an intelligent way. Each user manages their own bibliographical database in isolation from others. Target users use a common vocabulary for document indexing but may interpret the indexing vocabulary differently from others. Software agents are used to ensure indirect cooperation between users. A P2P architecture is used to allow users to control their data sharing scheme with others and to ensure their autonomy and privacy. The system associates a software assistant agent with each user. Agents are attributed three main skills: a) detecting the associated user’s hot topics, b) selecting a subset of peer agents that are likely to provide relevant recommendations, and c) recommending both documents and other agents in response to a recommendation request sent by a peer agent. The last two skills are handled by implementing two inter-related data-driven case-based reasoning systems. The basic idea underlying the document recommendation process is to map hot topics sent by an agent to local topics. Documents indexed by mapped topics are then recommended to the requesting agent. This agent will provide later, a relevance feedback computed after the user evaluation of the received recommendations. Provided feedbacks are used to learn to associate a community of peer agents to each local hot topic. An experimental study involving one hundred software agents using real bibliographical data is described. The Obtained results demonstrate the validity of the proposed approach.

State-of-the-Art Recommender Systems

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.

Adaptive User Profiles

A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant, personalized, and context-sensitive information delivery. Yet traditional approaches to user profiles rely on explicit, brittle models that go out of date very quickly, lack relevance, and have few natural connections to related models. In this chapter the authors show how it is possible to create adaptive user profiles without any explicit input at all. Rather, leveraging implicit behaviour on social information networks, the authors can create profiles that are both adaptive and socially connective. Such profiles can help provide personalized access to enterprise resources and help identify other people with related interests.