Cold-Start Management with Cross-Domain Collaborative Filtering and Tags

Abstract. Recommender systems suffer from the new user problem, i.e., the difficulty to make accurate predictions for users that have rated only few items. Moreover, they usually compute recommendations for items just in one domain, such as movies, music, or books. In this paper we deal with such a cold-start situation exploiting cross-domain recommendation techniques, i.e., we suggest items to a user in one target domain by using ratings of other users in a, completely disjoint, auxiliary domain. We present three rating prediction models that make use of information about how users tag items in an auxiliary domain, and how these tags correlate with the ratings to improve the rating prediction task in a different target domain. We show that the proposed techniques can effectively deal with the considered cold-start situation, given that the tags used in the two domains overlap

Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Context-Aware Recommender System (CARS) models are trained on datasets of context-dependent user preferences (ratings and context information). Since the number of context-dependent preferences increases exponentially with the number of contextual factors, and certain contextual in- formation is still hard to acquire automatically (e.g., the user's mood or for whom the user is buying the searched item) it is fundamental to identify and acquire those factors that truly in uence the user preferences and the ratings. In particular, this ensures that (i) the user e ort in specifying contextual information is kept to a minimum, and (ii) the system's performance is not negatively impacted by irrele- vant contextual information. In this paper, we propose a novel method which, unlike existing ones, directly estimates the impact of context on rating predictions and adaptively identi es the contextual factors that are deemed to be useful to be elicited from the users. Our experimental evaluation shows that it compares favourably to various state-of-the-art context selection methods

Techniques for cold-starting context-aware mobile recommender systems for tourism

Abstract. Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem

Switching hybrid for cold-starting context-aware recommender systems

Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.Peer Reviewe

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

Health economic assessment of ferric carboxymaltose in patients with iron deficiency and chronic heart failure based on the FAIR-HF trial: an analysis for the UK

AIMS: The purpose of this study was to evaluate the cost-effectiveness of iron repletion using intravenous (i.v.) ferric carboxymaltose (FCM) in chronic heart failure (CHF) patients with iron deficiency with or without anaemia. Cost-effectiveness was studied from the perspective of the National Health Service in the UK. METHODS AND RESULTS: A model-based cost-effectiveness analysis was used to compare iron repletion with FCM with no iron treatment. Using data from the FAIR-HF trial and publicly available sources and publications, per patient costs and clinical effectiveness of FCM were estimated compared with placebo. Cost assessment was based on study drug and administration costs, cost of CHF treatment, and hospital length of stay. The incremental cost-effectiveness ratio (ICER) of FCM use was expressed as cost per quality-adjusted life year (QALY) gained, and sensitivity analyses were performed on the base case. The time horizon of the analysis was 24 weeks. Mean QALYs were higher in the FCM arm (difference 0.037 QALYs; bootstrap-based 95% confidence interval 0.017-0.060). The ICER of FCM compared with placebo was €4414 per QALY gained for the FAIR-HF dosing regimen. Sensitivity analyses confirmed the base case result to be robust. CONCLUSION: From the UK payers' perspective, managing iron deficiency in CHF patients using i.v. FCM was cost-effective in this analysis. The base case ICER was clearly below the threshold of €22 200-€33 300/QALY gained (£20 000-£30 000) typically used by the UK National Institute for Health and Clinical Excellence and proved to be robust in sensitivity analysis. Improved symptoms and better quality of life contributed to this result

Switching hybrid for cold-starting context-aware recommender systems

Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems.Peer Reviewe

Personality-based active learning for collaborative filtering recommender systems

Abstract. Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality- using the Five Factor Model (FFM)- in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.