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In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users’ actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics.

This paper proposes the architecture of an agent-based hybrid recommender system consists of three subsystems: content-based, collaborate filtering and rating prediction. They are controlled by a task agent, who self-determines when to execute the hybrid recommendation algorithm based on user’s implicit feedback and its knowledge base. In content-based subsystem, an improved implicit feedback algorithm is presented. Moreover, an improved inter-user similarity function is applied to find out like-minded neighbors in collaborate filtering subsystem. To enhance the quality, result sets of content-based and collaborate filtering recommendations are reunited to select the top 5 items through our rating prediction algorithm based on linear regression model. Afterwards, a client agent sends back recommendations and receives another user’s request. Experimental data show that hybrid techniques hold the promise of produce high-quality recommendations with the assistance of agents.

The process of collecting preferences from users is fundamental during the normal operational life of a recommender system. The preference elicitation strategy can affect both the “user utility” (how well the system can make good recommendations to the new user who is undergoing the elicitation process) and the “system utility” (how well the system can provide good recommendations to all users, given what it learns from the new users). Not only do recommender systems need to gather information from users; they also need this information to be reliable and noiseless, as inconsistencies in user preferences limit prediction accuracy…