NDLI: CMAP: effective fusion of quality and relevance for multi-criteria recommendation

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CMAP: effective fusion of quality and relevance for multi-criteria recommendation

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CMAP: effective fusion of quality and relevance for multi-criteria recommendation

Content Provider	ACM Digital Library
Author	Xin, Xin King, Irwin Lyu, Michael R.
Abstract	The research issue of recommender systems has been treated as a classical regression problem over the decades and has obtained a great success. In the next generation of recommender systems, multi-criteria recommendation has been predicted as an important direction. Different from traditional recommender systems that aim particularly at recommending high-quality items evaluated by users' ratings, inmulti-criteria recommendation, quality only serves as one criterion, and many other criteria such as relevance, coverage, and diversity should be simultaneously optimized. Although recently there is work investigating each single criterion, there is rarely any literature that reports how each single criterion impacts each other and how to combine them in real applications. Thus in this paper, we study the relationship of two criteria, quality and relevance, as a preliminary work in multi-criteria recommendation. We first give qualitative and quantitative analysis of competitive quality-based and relevance-based algorithms in these two criteria to show that both algorithms cannot work well in the opposite criteria. Then we propose an integrated metric and finally investigate how to combine previous work together into an unified model. In the combination, we introduce a Continuous-time MArkov Process (CMAP) algorithm for ranking, which enables principled and natural integration with features derived from both quality-based and relevance-based algorithms. Through experimental verification, the combined methods can significantly outperform either single quality-based or relevance-based algorithms in the integrated metric and the CMAP model outperforms traditional combination methods by around 3%. Its linear complexity with respect to the number of users and items leads to satisfactory performance, as demonstrated by the around 7-hour computational time for over 480k users and almost 20k items.
Starting Page	455
Ending Page	464
Page Count	10
File Format	PDF
ISBN	9781450304931
DOI	10.1145/1935826.1935895
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Continuous-time markov process Collaborative filtering Recommender system
Content Type	Text
Resource Type	Article

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