Collaborative filtering (CF) is an important approach for recommendation system which is widely used in a great number of aspects of our life, heavily in the online-based commercial systems. One popular algorithms in CF is the K-nearest neighbors (KNN) algorithm, in which the similarity measures are used to determine nearest neighbors of a user, and thus to quantify the dependency degree between the relative user/item pair. Consequently, CF approach is not just sensitive to the similarity measure, yet it is completely contingent on selection of that measure. While Jaccard - as one of those commonly used similarity measures for CF tasks - concerns the existence of ratings, other numerical measures such as cosine and Pearson concern the magnitude of ratings. Particularly speaking, Jaccard is not a dominant measure, but it is long proven to be an important factor to improve any measure. Therefore, in our continuous efforts to find the most effective similarity measures for CF, this research focuses on proposing new similarity measure via combining Jaccard with several numerical measures. The combined measures would take the advantages of both existence and magnitude. Experimental results on, Movie-lens dataset, showed that the combined measures are preeminent outperforming all single measures over the considered evaluation metrics.

Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialog. A common approach in the literature to drive such dialogs is to incrementally ask users about their preferences regarding desired and undesired item features or regarding individual items. A central research goal in this context is efficiency, evaluated with respect to the number of required interactions until a satisfying item is found. This is usually accomplished by making inferences about the best next question to ask to the user. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions is better than another one in a given application. With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation. This model, which is designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, in particular with respect to the computational complexity of devising optimal interaction strategies. Through such a theoretical analysis we show that finding an efficient conversational strategy is NP-hard, and in PSPACE in general, but for particular kinds of catalogs the upper bound lowers to POLYLOGSPACE. From a practical point of view, this result implies that catalog characteristics can strongly influence the efficiency of individual conversational strategies and should therefore be considered when designing new strategies. A preliminary empirical analysis on datasets derived from a real-world one aligns with our findings.

The Oversight Board has only been up and running for less than a year, but Facebook says it's already having trouble keeping up with the group's recommendations. The company says it wants to work with the Oversight Board to "improve the recommendation process," though it's not yet clear what those changes might entail. But it suggests Facebook is looking to shake up the way it deals with the independent body it created to oversee its content policies. In a new report detailing Facebook's dealings with the Oversight Board, the company notes that it's made significant changes as the result of the Oversight Board's recommendations. These changes include updates to how it handles hate speech and nudity, as well as how it determines "newsworthy" content.

In a content-based approach to recommendation, a lot of information is available for both items and users which is useful to create profiles. We used a graph model to represent these profiles, connecting each item to its features and each user to features of interest. Even the nearest neighbor network was built using only this information. The collaborative filtering approach, on the other hand, relies on data related to the different kinds of interactions between users and items. Such information is generally referred to as a user–item dataset.

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

Reading groups domain is a new domain for group recommenders. In this paper we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups, for reading groups domain. BoRGo uses a new information filtering technique which uses the difference between positive and negative feedbacks about a feature of a user profile and also presents an interface for after recommendation processes like achieving a consensus on the reading list.