Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity (original) (raw)

XploDiv: Diversification Approach for Recommender Systems

2015

Recommender Systems have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional Recommender Systems usually do not produce diverse results, though it has been argued that diversity is a desirable feature. The study of diversity aware Recommender Systems has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval. However, we argue it is not enough to adapt Information Retrieval techniques towards Recommender Systems, as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to Recommender Systems. In this report, we propose a diversification technique for Recommender Systems that generates a diversified list of results which not only balances the trade-off between quality (in terms of accuracy) and diversit...

Improving recommendation diversity

2001

Abstract Recommender systems offer users a more intelligent and personalised mechanism to seek out new information. Content-based recommender systems generally prefer to retrieve a set of items maximally similar to a users' query and/or profile. We argue that as new types of recommendation domains and tasks emerge, this blind faith in the similarity assumption begins to seem flawed.

Solving the apparent diversity-accuracy dilemma of recommender systems

Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.

Adaptive Diversity in Recommender Systems

2015

The evaluation of a recommendation engine cannot rely only on the accuracy of provided recommendations. One should consider additional dimensions, such as diversity of provided suggestions, in order to guarantee heterogeneity in the recommendation list. In this paper we analyse users’ propensity in selecting diverse items, by taking into account content-based item attributes. Individual propensity to diversification is used to re-rank the list of Top-N items predicted by a recommendation algorithm, with the aim of fostering diversity in the final ranking. We show experimental results that confirm the validity of our modelling approach.