We propose an improved learning model for non-negative matrix factorization in the context-aware recommendation. We extend the collective non-negative matrix factorization through hybrid regularization method by combining multiplicative update rules with Barzilai-Borwein optimization. This provides new improved way of learning factorized matrices. We combine ratings, content features, and contextual information in three different 2-dimensional matrices. We study the performance of the proposed method on recommending top-N items. The method was empirically tested on 4 datasets, including movies, music, and mobile apps, showing an improvement in comparison with other state-of-the-art for top-N recommendations, and time convergence to the stationary point for larger datasets.

Social Gaming is a pervasive phenomenon, driven by the advent of social networks and the digitization of game dis-tribution. This paper positions and defines Casual Social Games (CSGs) as a genre and platform agnostic subset of Social Games that incorporates browser, mobile, console and wearable digital games. The authors argue that – as CSGs impact the games industry, shape play patterns and audience characteristics, and proliferate to new platforms – understanding and measuring their social aspect becomes highly relevant. A randomized experiment on added social gameplay in a CSG on both mobile and Facebook serves to support this argument. Experimental results highlight that social gameplay is extremely important for engagement and monetization in casual games, even more so on mobile plat-forms. This does not only suggest that CSG developers will benefit from focusing on increased social interaction in their games, but that Game Analytics should strive to unify defi-nitions and build a common body of knowledge around the social aspect of casual gaming.

Context personalisation is a flourishing area of research with many applications. Context personalisation systems usually employ a user model to predict the appeal of the context to a particular user given a history of interactions. Most of the models used are context-dependent and their applicability is usually limited to the system and the data used for model construction. Establishing models of user experience that are highly scalable while maintaing the performance constitutes an important research direction. In this paper, we propose generic models of user experience in the computer games domain. We employ two datasets collected from players interactions with two games from different genres where accurate models of players experience were previously built. We take the approach one step further by investigating the modelling mechanism ability to generalise over the two datasets. We further examine whether generic features of player behaviour can be defined and used to boost the modelling performance. The accuracies obtained in both experiments indicate a promise for the proposed approach and suggest that game-independent player experience models can be built.

Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.

We present a demonstration of PaTux, an authoring tool for designing levels in SuperTux game through combining patterns. PaTux allows game designers to specify the design of their levels using patterns extracted from training level samples. The Non-negative Matrix Factorisation (NMF) method is utilised to approximate pattern and weight matrices from the training data. The patterns are visualised for designers to choose from and the changes made on the level structure are visualised in realtime. The designer can also specify the weight of each pattern permitting exploration of a wider variety. The data used to train the model can also be specified by the designer resulting in learning a new set of patterns. The system also suggests variations for a given design. When the designer is satisfied with the design, the system allows loading the resultant level in the game to be played.

In this paper we present a procedural content generator using Non-negative Matrix Factorisation (NMF). We use representative levels from five dissimilar content generators to train NMF models that learn patterns about the various components of the game. The constructed models are then used to automatically generate content that resembles the training data as well as to generate novel content through exploring new combinations of patterns. We describe the methodology followed and we show that the generator proposed has a more powerful capability than each of generator taken individually. The generator's output is compared to the other generators using a number of expressivity metrics. The results show that the proposed generator is able to resemble each individual generator as well as demonstrating ability to cover a wider and more novel content space.

Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstarte the effectiveness of our approaches.

Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query users about their preferences and offer recommendations based on the system's belief about the user's utility function. We analyze the connection between the problem of generating optimal recommendation sets and the problem of generating optimal choice queries, considering both Bayesian and regret-based elicitation. Our results show that, somewhat surprisingly, under very general circumstances, the optimal recommendation set coincides with the optimal query.

We present a novel approach for identifying exact and approximate behavioral equivalence between models of agents. This is significant because both decision making and game play in multiagent settings must contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the model space is to group models that are behaviorally equivalent. Identifying equivalence between models requires solving them and comparing entire policy trees. Because the trees grow exponentially with the horizon, our approach is to focus on partial policy trees for comparison and determining the distance between updated beliefs at the leaves of the trees. We propose a principled way to determine how much of the policy trees to consider, which trades off solution quality for efficiency. We investigate this approach in the context of the interactive dynamic influence diagram and evaluate its performance.