Recommender System Based on Consumer Product Reviews

Universitat de Girona, Girona, Catalonia, Spain
01/2006; DOI: 10.1109/WI.2006.144
Source: OAI


Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

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    • ", and there is literature on how such explicit user feedback can be applied to recommender systems [Garcia Esparza et al. 2012; Aciar et al. 2006]. Furthermore , Desrosiers and Karypis [2010] proposed an alternative algorithm for computing similarities—a key function of collaborative-based recommender systems—using nonnumerical ratings. "

    Transactions on Interactive Intelligent Systems 06/2014; 4(2):1-26. DOI:10.1145/2512208
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    • "Moreover, they also comment on the advantages of using user-generated content for recommender systems; such as, for example, providing a better rationale for recommended products and increasing user trust in the system. One of the first attempts to build a recommender system based on user-generated review data is described in [1]. Here, an ontology is used to extract concepts from camera reviews and recommendations are provided based on users' requests about a product; for example, " I would like to know if Sony361 is a good camera, specifically its interface and battery consumption " . "
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    ABSTRACT: Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.
    Knowledge-Based Systems 05/2012; 29:3-11. DOI:10.1016/j.knosys.2011.07.007 · 2.95 Impact Factor
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    • "While community convergence in semantic annotation allows us to place an item in some semantic space, for many rating systems it is not immediately clear that evaluative annotations do the same for the item in 'taste' space (unless of course the rating system explicitly defines the semantic dimensions being evaluated, e.g. [1]). In many online communities, the rating system is only a one-dimensional, undefined scale. "
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    ABSTRACT: Social content-sharing networks allow users to share content annotations. Although convergence and consistency in semantic annotation (tags) has been well-studied, less effort has been devoted to studying evaluative annotations (ratings and reviews) with respect to user characteristics and user-item relationships. In this paper, we first identify trends in both item scores and in the ways in which users allocate scores, these are also associated with some of users' other activity -- in particular, social links and rating activity are both found to be higher for a moderate level of non-conformism and dissensus. We then conduct a more thorough investigation into how item score distributions might arise and be sustained. The fact that most items have a clear modal score can be attributed to the tendency of items to evoke similar degrees of satisfaction across users. In addition to this however, our findings suggest that social links between users can play a role in stabilising rating distributions. Firstly, we find that socially linked users are more likely to give the same score to an item (possibly due to similarities in taste). Secondly, we eliminate the possibility that distributions of scores arise through attracting users with particular ratings styles (e.g. tendency to agree). Thirdly, we find that a large mean shift is much rarer for items with a large proportion of added scores from socially linked users and that this is most likely to be due to maintaining a stable distribution than to added scores conforming to the mean.
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