User language model for collaborative personalized search

Abstract

Traditional personalized search approaches rely solely on individual profiles to construct a user model. They are often confronted by two major problems: data sparseness and cold-start for new individuals. Data sparseness refers to the fact that most users only visit a small portion of Web pages and hence a very sparse user-term relationship matrix is generated, while cold-start for new individuals means that the system cannot conduct any personalization without previous browsing history. Recently, community-based approaches were proposed to use the group's social behaviors as a supplement to personalization. However, these approaches only consider the commonality of a group of users and still cannot satisfy the diverse information needs of different users. In this article, we present a new approach, called collaborative personalized search. It considers not only the commonality factor among users for defining group user profiles and global user profiles, but also the specialties of individuals. Then, a statistical user language model is proposed to integrate the individual model, group user model and global user model together. In this way, the probability that a user will like a Web page is calculated through a two-step smoothing mechanism. First, a global user model is used to smooth the probability of unseen terms in the individual profiles and provide aggregated behavior of global users. Then, in order to precisely describe individual interests by looking at the behaviors of similar users, users are clustered into groups and group-user models are constructed. The group-user models are integrated into an overall model through a cluster-based language model. The behaviors of the group users can be utilized to enhance the performance of personalized search. This model can alleviate the two aforementioned problems and provide a more effective personalized search than previous approaches. Large-scale experimental evaluations are conducted to show that the proposed approach substantially improves the relevance of a search over several competitive methods.

The neglected user in music information retrieval research

“…This information is fed back into the SOM's codebook, and the mapping is adapted accordingly. Xue et al (2009) present a collaborative personalized search model that alleviates the problems of data sparseness and cold-start for new users by combining information on different levels (individual, interest group, and global). Although not explicitly targeted at music retrieval, the idea of integrating data about the user, his peer group, and global data to build a social retrieval model might be worth considering for MIR purposes.…”

Personalization in Multimodal Music Retrieval

“…This information is fed back into the SOM's codebook, and the mapping is adapted accordingly. [79] presents a collaborative personalized search model that alleviates the problems of data sparseness and cold-start for new users by combining information on different levels (individuals, interest groups, and global). [80,81] present CompositeMap, a model that takes into account similarity aspects derived from music content as well as from social factors.…”

“…The user model itself can also incorporate data on different levels of user representation. For example, [79] proposes a user model that comprises an individual model, a interest group model, and a global user model. We suggest adding a forth model, namely a cultural user model, that reflects the cultural area of the user.…”

“…To this end, it is necessary to apply and refine machine learning techniques to learn user preferences, i.e., a mapping between individual, user-specific factors and the user's appeal to certain music categories, styles, or individual artists or tracks. In this step, various models of different scope and complexity need to be evaluated: for example, one model that takes only directly user-related data into account, another one similar to [79] that represents an integrated model comprising of an individual user model, a group model (cultural / peer group), and a global model.…”

Building Rich User Search Queries Profiles

“…Mei and Church [12] study personalization through backoff, i.e., including information from similar users or from a user's group for estimating the likelihood of a document click for the given user. Morris et al, Teevan et al, and Xue et al study several further techniques based on user groups [14], [21], [22]. Dou et al [4] analyze when and how personalization is useful by using click-based and query-based personalization techniques.…”