Local Popularity and Time in top-N Recommendation

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.Comment: ECIR short paper, 7 page

Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few if any implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency. In particular, we present two such context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types or categories that are typically purchased repetitively (collectibles, grocery goods) or once (household appliances). Experiments performed on three implicit datasets (two proprietary ones and an implicit variant of the Netflix dataset) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012, Bristol, U

Putting the Social in Social Media: How Human Connection Triggers Engagement

Social media has become the preferred channel of information and has altered patterns of interaction and connection. As a result, society now revolves around a two-way form of communication with constant dialogue and instant responses. Public relations practitioners have had to adapt and change their strategy in order to keep up with the times, and because of this, engagement is now considered to be a measurement of success. In terms of social media, engagement is how users interact with content and participate in online conversations. This study will uncover what causes people to engage on social media and identify the characteristics that make a photo and a video interesting

A contextual modeling approach for model-based recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_5Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations

Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach

An approach to defining actionability as a measure of interestingness of patterns is proposed. This approach is based on the concept of an action hierarchy which is defined as a tree of actions with patterns and pattern templates (data mining queries) assigned to its nodes. A method for discovering actionable patterns is presented and various techniques for optimizing the discovery process are proposed.Information Systems Working Papers Serie

Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013.Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542

Electron and trap dynamics in As-ion-implanted and annealed GaAs

The ultrafast dynamics of As-ion-implanted and annealed GaAs is investigated using transmission pump–probe measurements.Carrier recombination time was found to increase from 4 to 40 ps with increasing annealing temperature. At lower annealing temperatures, the transmitted optical signal is dominated by induced absorption and at higher annealing temperatures this effect is replaced by induced transparency.This work was supported in part by the EC INCOCOPERNICUS project ‘‘DUO—devices for ultrafast optoelectronics’’ and the Lithuanian Science and Study Foundation

RQL: A Query Language For Recommender Systems

Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years, both in the industry and academia. The traditional two-dimensional approach to recommender systems, involving the dimensions of Users and Items, has been subsequently extended to the multidimensional approach supporting additional contextual dimensions and OLAP-type aggregation capabilities. Furthermore, the class of all possible recommendations available to the users in traditional recommender systems is typically determined by the vendor and is quite limited. In this paper we address this limitation by proposing a query language RQL that allows the users to formulate various types of recommendation requests on their own. RQL adapts OLAP queries to the domain of recommender systems and, therefore, is able to support both the traditional two-dimensional and the more complex multidimensional recommender systems. The paper also presents a recommendation algebra that allows mapping RQL queries into the algebraic expressions for the query processing purposes. Finally, the paper presents a method for executing RQL queries.Information Systems Working Papers Serie