Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/119500
Title: Interactive Music Recommendation: Context,Content and Collaborative Filtering
Authors: WANG XINXI
Keywords: music recommendation, recommendation system, music content analysis, machine learning, context-aware, mobile
Issue Date: 3-Dec-2014
Citation: WANG XINXI (2014-12-03). Interactive Music Recommendation: Context,Content and Collaborative Filtering. ScholarBank@NUS Repository.
Abstract: Music recommendation systems predict users' preferred songs and thus greatly ease the process of music selection and also boost the revenue of online music merchants. However, results produced by existing music recommenders are still not satisfactory because of their ignorance of relevant information or the drawbacks of their underlying modeling techniques. To better satisfy users' music needs, this thesis strives to improve recommendation performance from three aspects. First, we developed the first context-aware music recommendation system that recommends songs to match the target user's daily activities including sleeping, running, studying, working, walking and shopping. Second, we present a new approach to music recommendation by formulating the exploration-exploitation trade-off in music recommendation as an interactive reinforcement learning task. Extensive evaluations are conducted to demonstrate the effectiveness of the developed methods. Third, we developed a model that simultaneously learns features from audio content and makes personalized recommendations based on deep belief network. The features are then incorporated into collaborative filtering to form an effective hybrid recommendation method.
URI: http://scholarbank.nus.edu.sg/handle/10635/119500
Appears in Collections:Ph.D Theses (Open)

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