Evaluation of Item-Based Top-N Recommendation Algorithms
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For recommender systems that base their product rankings primarily on a measure of similarity between items and the user query, it can often happen that products on the recommendation list are highly similar to each other and lack diversity. In this article we argue that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and introduce a methodology to evaluate their performance in terms of novel item retrieval. Moreover, noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we formulate the trade-off between diversity and matching quality as a binary optimization problem, with an input control parameter allowing explicit tuning of this trade-off. We study solution strategies to the optimization problem and demonstrate the importance of the control parameter in obtaining desired system performance. The methods are evaluated for collaborative recommendation using two datasets and case-based recommendation using a synthetic dataset constructed from the public-domain Travel dataset.