Engineering Applications of Artificial Intelligence
An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles☆
Introduction
Discovering products that meet the needs of the consumers are crucial in such competitive environments as online shopping. Recommender systems assist in advertising tasks by automatically selecting the most appropriate items for each user as per his/her personal interests and preferences (Adomavicius and Tuzhilin, 2005). Research in recommender systems started back in the early 1990s, but the greatest advances have been due to the irruption of recent technologies like those of the Semantic Web (Berners-Lee et al.,). It has been proved that semantics-based recommender systems can outperform previous approaches by exploiting two main elements:
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a knowledge base – typically an ontology – that represents semantic features or attributes of the available items, and
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filtering strategies based on semantic reasoning techniques that discover relevant relationships between the users' preferences and the items to be recommended (see examples in Hung, 2005, Middleton et al., 2004, Yuan and Cheng, 2004, Blanco-Fernández et al., 2008, Pazos-Arias José et al., 2008, Blanco-Fernández et al., 2010).
Obviously, keeping the users' satisfaction high requires means to adapt the selection of items as their interests evolve over time. For many years, in most of the existing filtering strategies, data collection about the users' interests was regarded as a static process, weighing equally the ratings given by the users at different times. Later, some researchers proposed time-aware approaches that made the last observations more significant than the older ones, which means assuming that a user's interest in a product always decreases from the moment of the last purchase (see examples in Maloof and Michalski, 2000, Schwab et al., 2001, Duen-Ren and Ya-Yueh, 2005, Ding and Li, 2005, Lee and Park, 2009). This may be true in certain areas of application, such as personalized programming guides that recommend TV programs to the users. Notwithstanding, the interest in (or the need for) commercial products in general may actually increase or vary in diverse forms over time. For example, if a user has just bought a dishwasher, it is foreseeable that he/she will not need another one until the average lifetime of such appliances has passed; therefore, the interest estimations should follow an increasing function, and any recommender system should prioritize other products for some time. Likewise, the interest for seasonal clothes may vary along the year, while the interest in books and music may remain constant and school equipment may have a peak at the beginning of the academic year.
The main research contribution of this paper is an improvement to the current filtering strategies, aimed at increasing the effectiveness of semantics-based recommender systems in online shopping. The basic assumption is that the influence of time can be radically different not only for different types of items as explained above, but also for different users. For instance, whereas car tires typically have a lifetime of 6 years for average drivers, it is expectable that taxi drivers or users interested in car tuning and motor sports need more frequent replacements (say, every 6 months). Analogously, it makes sense not to recommend dolls for some time after an average user has bought one, but the same is not true for doll collectors. Briefly speaking, our new approach makes tailor-made selections of items by exploiting the semantics formalized in an ontology to link items (and their features) to time functions, whose shapes are corrected by considering the preferences of like-minded individuals and the effects of time in their purchasing behaviors.
The paper is organized as follows. Section 2 includes a review of recommender systems literature to highlight the differences between the management of time in previous works and in our new filtering strategy. Next, Section 3 details the main parts of our personalization framework, while in Section 4 we focus on the algorithmic internals of our time-aware filtering strategy. Section 5 presents the results of experiments we have carried out (with real users) to assess the personalization quality achieved by the new filtering strategy in comparison with existing approaches. Finally, Section 6 provides a summary of conclusions and the motivation of our ongoing work.
Section snippets
Related work
Research in recommender systems is hectic nowadays, in an attempt to address the many new questions raised by the growing number of practical applications. Next, we provide an overview of the milestones in recommenders history, and thereafter focus on the issues of producing time-aware recommendations in online shopping, which remain practically unexplored in literature.
Our personalization framework
This section describes the main elements of our new personalization framework: the domain ontology and the parameterized time functions attached to its nodes; the individual user profiles and the stereotypes that model the preferences of groups of users; and the group corrections that modify the default time dependence curves. The new time-aware filtering strategy enabled by these elements will be presented in Section 4.
Our filtering strategy
Having introduced the elements of our personalization framework, we can now describe our new semantics-based time-aware filtering strategy, which follows a four-step process as depicted in Fig. 4:
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Step 1: Stereotype-driven pre-filtering. Initially, we perform an offline pre-filtering process driven by the available stereotypes, using a semantics-based similarity metric to sort out the different items by their potential interest for different groups of users.
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Step 2: Classification of the user
Experiments and evaluation
We have made experiments in laboratory to corroborate two research hypotheses:
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First, we postulate that the consideration of time helps improve the accuracy of the recommendations. To validate this hypothesis, we compared the success of the recommendations made by three filtering approaches, which we will be referring to as follows:
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Purely reasoning-based filtering (abbreviated as PRBF): as detailed in López-Nores et al. (2010), this approach relies on semantic reasoning mechanisms to make
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Conclusions and future work
As e-commerce provides an increasingly more powerful gateway for shopping online, serving customers instantly and efficiently requires to recognize their particular needs, to recommend personalized shopping lists, and to adapt these lists as the users' interests change over time. Existing recommender systems miss an important point in the adaptation process, related to the fact that the influence of time can vary greatly for users with very different preferences. This paper introduces an
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Work funded by the Ministerio de Educación y Ciencia (Gobierno de España) research project TIN2010-20797.