In user memory based collaborative filtering algorithm, recommendation quality depends strongly on the neighbors selection which is a high computation complexity task in large scale datasets.A common approach to overpass this limitation consists of clustering users into groups of similar profiles and restrict neighbors computation to the cluster that includes the target user. K-means is a popular clustering algorithms used widely for recommendation but initial seeds selection is still a hard complex step. In this paper a new genetic algorithm encoding is proposed as an alternative of k-means clustering. The initialization issue in the classical k-means is targeted by proposing a new formulation of the problem, to reduce the search space complexity affect as well as improving clustering quality. We have evaluated our results using different quality measures. The employed metrics include rating prediction evaluation computed using mean absolute error. Additionally, we employed both of precision and recall measures using different parameters. The obtained results have been compared against baseline techniques which proved a significant enhancement.

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There exist many research works that strive to answer the question “what news article is a user going to click next given his profile”. These works take into account the time dimension to reveal users’ preferences over time. However, few works exploit adequately the information that is hidden inside user sessions. User sessions include a list of user interactions with items within a short period of time such as 30 min, and can reveal her very last intentions. In this paper, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals. Thus, we are able to better capture the similarities among items that are co-selected inside a user session but also within any two consecutive sessions. We have evaluated experimentally our method and compare it against state-of-the-art algorithms on three real-life datasets. We demonstrate the superiority of our method over its competitors.

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