Open Access

Recommender systems are personalized information systems that learn individual preferences from interacting with users. Recommender systems use machine learning techniques to compute suggestions for the users. Supervised machine learning relies on optimizing for a suitable objective function. Suitability means here that the function actually reflects what users and operators consider to be a good system performance. Most of the academic literature on recommendation is about rating prediction. For two reasons, this is not the most practically relevant prediction task in the area of recommender systems: First, the important question is not how much a user will express to like a given item (by the rating), but rather which items a user will like. Second, obtaining explicit preference information like ratings requires additional actions from the side of the user, which always comes at a cost. Implicit feedback in the form of purchases, viewing times, clicks, etc., on the other hand, is abundant anyway. Very often, this implicit feedback is only present in the form of positive expressions of preference. In this work, we primarily consider item recommendation from positive-only feedback. A particular problem is the suggestion of new items -- items that have no interaction data associated with them yet. This is an example of a cold-start scenario in recommender systems. Collaborative models like matrix factorization rely on interaction data to make predictions. We augment a matrix factorization model for item recommendation with a mechanism to estimate the latent factors of new items from their attributes (e.g. descriptive keywords). In particular, we demonstrate that optimizing the latent factor estimation with regard to the overall loss of the item recommendation task is superior to optimizing it with regard to the prediction error on the latent factors. The idea of estimating latent factors from attributes can be extended to other tasks (new users, rating prediction) and prediction models, yielding a general framework to deal with cold-start scenarios. We also adapt the Bayesian Personalized Ranking (BPR) framework, which is state of the art in item recommendation, to a setting where more popular items are more frequently encountered when making predictions. By generalizing even more, we get Weighted Bayesian Personalized Ranking, an extension of BPR that allows importance weights to be placed on specific users and items. All method contributions are supported by experiments using large-scale real-life datasets from various application areas like movie recommendation and music recommendation. The software used for the experiments has been released as part of an efficient and scalable free software package.

In this paper, the idea of ubiquitous information retrieval is presented in a storytelling manner. Starting from a rough review of information retrieval system usage, some empirical hints on IR in everyday life are given. Ch. 4 explores the heterogeneity of interaction with IRS for one day in the life of a (common search engine) user. In ch. 5 summarizes these observations and suggests research approaches for modelling information retrieval as an essential component of interaction in the information society.

The learners’ motivation has an impact on the quality of learning, especially in e-Learning environments. Most of these environments store data about the learner’s actions in log files. Logging the users’ interactions in educational systems gives the possibility to track their actions at a refined level of detail. Data mining and machine learning techniques can “give meaning” to these data and provide valuable information for learning improvement. An area where improvement is absolutely necessary and of great importance is motivation, known to be an essential factor for preventing attrition in e-Learning. In this paper we investigate if the log files data analysis can be used to estimate the motivational level of the learner. A decision tree is build from a limited number of log files from a web-based learning environment. The results suggest that time spent reading is an important factor for predicting motivation; also, performance in tests was found to be a relevant indicator of the motivational level.

Class binarizations are effective methods that break multi-class problem down into several 2- class or binary problems to improve weak learners. This paper analyzes which effects these methods have if we choose a Naive Bayes learner for the base classifier. We consider the known unordered and pairwise class binarizations and propose an alternative approach for a pairwise calculation of a modified Naive Bayes classifier.

Authors of menu optimization methods often use navigation time prediction models without validating whether the model is adequate for the site and its users. We review the assumptions underlying navigation time prediction models and present a method to validate these assumptions offline. Experiments on four web sites show how accurate the various model features describe the behavior of the users. These results can be used to select the best model for a new optimization task. In addition, we find that the existing optimization methods all use suboptimal models. This indicates that our results can significantly contribute to more effective menu optimization.