Recommender systems help users find items of interest by tailoring their recommendations to users' personal preferences. The utility of an item for a user, however, may vary greatly depending on that user's specific situation or the context in which the item is used. Without considering these changes in preferences, the recommendations may match the general preferences of a user, but they may have small value for the user in his/her current situation. In this paper, we introduce a hierarchical hidden Markov model for capturing changes in user's preferences. Using a user's feedback sequence on items, we model the user as a hierarchical hidden Markov process and the current context of the user as a hidden variable in this model. For a given user, our model is used to infer the maximum likelihood sequence of transitions between contextual states and to predict the probability distribution for the context of the next action. The predicted context is then used to generate recommendations. Our evaluation results using Last.fm music playlist data, indicate that this approach achieves significantly better performance in terms of accuracy and diversity compared to baseline methods.

Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during the course of users' interactions with the system. In this paper, we introduce an interactive recommender system that can detect and adapt to changes in context based on the user's ongoing behavior. The system, then, dynamically tailors its recommendations to match the user's most recent preferences. We formulate this problem as a multi-armed bandit problem and use Thompson sampling heuristic to learn a model for the user. Following the Thompson sampling approach, the user model is updated after each interaction as the system observes the corresponding rewards for the recommendations provided during that interaction. To generate contextual recommendations, the user's preference model is monitored for changes at each step of interaction with the user and is updated incrementally. We will introduce a mechanism for detecting significant changes in the user's preferences and will describe how it can be used to improve the performance of the recommender system.

Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly.

Feature Models (FMs) are used extensively in software product line engineering to help generate and validate individual product configurations and to provide support for domain analysis. As FM construction can be tedious and time-consuming, researchers have previously developed techniques for extracting FMs from sets of formally specified individual configurations, or from software requirements specifications for families of existing products. However, such artifacts are often not available. In this paper we present a novel, automated approach for constructing FMs from publicly available product descriptions found in online product repositories and marketing websites such as SoftPedia and CNET. While each individual product description provides only a partial view of features in the domain, a large set of descriptions can provide fairly comprehensive coverage. Our approach utilizes hundreds of partial product descriptions to construct an FM and is described and evaluated against antivirus product descriptions mined from SoftPedia.

Domain analysis is a labor-intensive task in which related software systems are analyzed to discover their common and variable parts. Many software projects include extensive domain analysis activities, intended to jumpstart the requirements process through identifying potential features. In this paper, we present a recommender system that is designed to reduce the human effort of performing domain analysis. Our approach relies on data mining techniques to discover common features across products as well as relationships among those features. We use a novel incremental diffusive algorithm to extract features from online product descriptions, and then employ association rule mining and the (k)-nearest neighbor machine learning method to make feature recommendations during the domain analysis process. Our feature mining and feature recommendation algorithms are quantitatively evaluated and the results are presented. Also, the performance of the recommender system is illustrated and evaluated within the context of a case study for an enterprise-level collaborative software suite. The results clearly highlight the benefits and limitations of our approach, as well as the necessary preconditions for its success.

Rapid prototypes are often developed early in the software development process in order to help project stakeholders explore ideas for possible features, and to discover, analyze, and specify requirements for the project. As prototypes are typically thrown-away following the initial analysis phase, it is imperative for them to be created quickly with little cost and effort. Tool support for finding and reusing components from open-source repositories offers a major opportunity to reduce this manual effort. In this paper, we present a system for rapid prototyping that facilitates software reuse by mining feature descriptions and source code from open-source repositories. Our system identifies and recommends features and associated source code modules that are relevant to the software product under development. The modules are selected such that they implement as many of the desired features as possible while exhibiting the lowest possible levels of external coupling. We conducted a user study to evaluate our approach and the results indicated that our proposed system returned packages that implemented more features and were considered more relevant than the state-of-the-art approach.

We present a recommender system that models and recommends product features for a given domain. Our approach mines product descriptions from publicly available online specifications, utilizes text mining and a novel incremental diffusive clustering algorithm to discover domain-specific features, generates a probabilistic feature model that represents commonalities, variants, and cross-category features, and then uses association rule mining and the k-Nearest-Neighbor machine learning strategy to generate product specific feature recommendations. Our recommender system supports the relatively labor-intensive task of domain analysis, potentially increasing opportunities for re-use, reducing time-to-market, and delivering more competitive software products. The approach is empirically validated against 20 different product categories using thousands of product descriptions mined from a repository of free software applications.

Predicting latency between nodes on the Internet can have a significant impact on the performance of many services that use latency distances among nodes as a decision making input. Coordinate-based approaches are among the family of latency prediction techniques where latency between each pair of nodes is modeled as the virtual distance among those nodes over a virtual system. This article proposes the decentralized coordinate system (DCS) as a fully distributed system that does not rely on any infrastructure support from the underlying network. DCS uses a two-phase algorithm: first, each host is assigned rough coordinates, then, the rough estimation is refined in a way that it gradually converges to the accurate relative position of nodes. Simulation results demonstrate that the accuracy of DCS is competitive with existing network coordinate systems and that it can be considered as a good alternative for some applications.