ABSTRACT
The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.
Supplemental Material
- O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan. Expected reciprocal rank for graded relevance. ACM CIKM '09 Conference Proceedings, pages 621--630, 2009. Google ScholarDigital Library
- C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, and I. MacKinnon. Novelty and Diversity in Information Retrieval Evaluation. ACM SIGIR'08 Conference Proceedings, page 659, 2008. Google ScholarDigital Library
- C. Desrosiers and G. Karypis. A Comprehensive Survey of Neighborhood-based Recommendation Methods. Recommender Systems Handbook, 54:107--144, 2011.Google ScholarCross Ref
- F. M. Harper and J. a. Konstan. The MovieLens Datasets : History and Context. 5(4):1--19, 2015. Google ScholarDigital Library
- T. Hofmann. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning, 42(1--2):177--196, 2001.Google Scholar
- T. Hofmann. Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems, 22(1):89--115, 2004. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative Filtering for Implicit Feedback Datasets. 2008.Google Scholar
- K. Jarvelin and J. Kekalainen. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information Systems, 20(4):422--446, 2002. Google ScholarDigital Library
- R. Santos, C. Macdonald, and I. Ounis. Exploiting Query Reformulations for Web Search Result Diversification. WWW, pages 881--890, 2010. Google ScholarDigital Library
- S. Vargas, P. Castells, and D. Vallet. Intent-oriented Diversity in Recommender Systems. ACM SIGIR'11 Conference Proceedings, pages 1211--1212, 2011. Google ScholarDigital Library
- C. X. Zhai, W. W. Cohen, and J. Lafferty. Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. 49(1):10--17, 2003.Google Scholar
Index Terms
- Intent-Aware Diversification Using a Constrained PLSA
Recommendations
Intent-aware Item-based Collaborative Filtering for Personalised Diversification
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and PersonalizationDiversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation ...
Learning aspect-level complementarity for intent-aware complementary recommendation
AbstractComplementary recommendation aims to recommend items that are dissimilar but relevant to, and likely purchased together with, the items a user has purchased. Although many efforts have been made, the existing works for complementary ...
Subprofile-aware diversification of recommendations
A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to ...
Comments