Musical recommendations and personalization in a social network

This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes in the user's behavior and needs in the real time. The core component of the algorithms is a taste graph containing information about different entities (users, tracks, artists, etc.) and relations between them (for example, user A likes song B with certainty X, track B created by artist C, artist C is similar to artist D with certainty Y and so on). Using the graph it is possible to select tracks a user would most probably like, to arrange them in a way that they match each other well, to estimate which items from a fixed list are most relevant for the user, and more. In addition, the paper describes the approach used to estimate algorithms efficiency and analyze the impact of different recommendation related features on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys 201

The benefits of opening recommendation to human interaction

This paper describes work in progress that uses an interactive recommendation process to construct new objects which are tailored to user preferences. The novelty in our work is moving from the recommendation of static objects like consumer goods, movies or books, towards dynamically-constructed recommendations which are built as part of the recommendation process. As a proof-of-concept we build running or jogging routes for visitors to a city, recommending routes to users according to their preferences and we present details of this system

Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. Existing CRS widely assumes that users have clear preferences. Under this assumption, the agent will completely trust the user feedback and treat the accepted or rejected signals as strong indicators to filter items and reduce the candidate space, which may lead to the problem of over-filtering. However, in reality, users' preferences are often vague and volatile, with uncertainty about their desires and changing decisions during interactions. To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS.VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem. In the VPMCR setting, we introduce an solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two main components: Uncertainty-aware Soft Estimation (USE) and Uncertainty-aware Policy Learning (UPL). USE estimates the uncertainty of users' vague feedback and captures their dynamic preferences using a choice-based preferences extraction module and a time-aware decaying strategy. UPL leverages the preference distribution estimated by USE to guide the conversation and adapt to changes in users' preferences to make recommendations or ask for attributes. Our extensive experiments demonstrate the effectiveness of our method in the VPMCR scenario, highlighting its potential for practical applications and improving the overall performance and applicability of CRS in real-world settings, particularly for users with vague or dynamic preferences

On User Modelling for Personalised News Video Recommendation

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems

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