Skipping-Based Collaborative Recommendations inspired from Statistical Language Modeling

Due to the almost unlimited resource space on the Web, efficient search engines and recommender systems have become a key element for users to find resources corresponding to their needs. Recommender systems aims at helping users in this task by providing them some pertinent resources according to their context and their profiles, by applying various techniques such as statistical and knowledge discovery algorithms. One of the most successful approaches is Collaborative Filtering, which consists in considering user ratings to provide recommendations, without considering the content of the resources; however the ratings are the only criterion taken into account to provide the recommendations, although including some other criterion should enhance their accuracy. One such criterion is the context, which can be geographical, meteorological, social, etc. In this chapter we focus on the temporal context, more specifically on the order in which the resources were consulted. The appropriateness of considering the order is domain dependent: for instance, it seems of little help in domains such as online moviestores, in which user transactions are barely sequential; however it is especially appropriate for domains such as Web navigation, which has a sequential structure. We propose to follow this direction for this domain, the challenge being to find a low enough complexity sequential model while providing a better accuracy. We first put forward similarities between Web navigation and natural language, and propose to adapt statistical language models to Web navigation to compute recommendations. Second, we propose a new model inspired from the n-gram skipping model. This model has several advantages: (1) It has both a low time and a low space complexity while providing a full coverage, (2) it is able to handle parallel navigations and noise, (3) it is able to perform recommendations in an anytime framework, (4) weighting schemes are used to alleviate the importance of distant resources. Third, we provide a comparison of this SLM inspired model to the state of the art in terms of features, complexity, accuracy and robustness and present experimental results. Tests are performed on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources highly improves the accuracy, and that the anytime configuration is able to provide a satisfying trade-off between an even lower computation time and a good accuracy while conserving a good coverage