We study recommendations in applications where there are temporal patterns in the way items are consumed or watched. For example, a student who has taken the Advanced Algorithms course is more likely to be interested in Convex Optimization, but a student who has taken Convex Optimization need not be interested in Advanced Algorithms in the future. Similarly, a person who has purchased the Godfather I DVD on Amazon is more likely to purchase Godfather II sometime in the future (though it is not strictly necessary to watch/purchase Godfather I beforehand). We propose a precedence mining model that estimates the probability of future consumption based on past behavior. We then propose Recsplorer: a suite of recommendation algorithms that exploit the precedence information. We evaluate our algorithms, as well as traditional recommendation ones, using a real course planning system. We use existing transcripts to evaluate how well the algorithms perform. In addition, we augment our experiments with a user study on the live system where users rate their recommendations.

Most recommendation methods are "hard-wired" into the system and support only fixed recommendations. The purpose of this demo is to show the expressivity of flexible recommendation workflows, how flexible recommendations can be processed over relational data, and to show flexible recommendations in action through a real system used for course planning.

Special-purpose social sites can offer valuable services to well-defined, closed, communities, e.g., in a university or in a corporation. The purpose of this demo is to show the challenges, special features and potential of a focused social system in action through CourseRank, a course evaluation and planning social system.

Recommendation systems have become very popular but most recommendation methods are `hard-wired' into the system making experimentation with and implementation of new recommendation paradigms cumbersome. In this paper, we propose FlexRecs, a framework that decouples the definition of a recommendation process from its execution and supports flexible recommendations over structured data. In FlexRecs, a recommendation approach can be defined declaratively as a high-level parameterized workflow comprising traditional relational operators and new operators that generate or combine recommendations. We describe a prototype flexible recommendation engine that realizes the proposed framework and we present example workflows and experimental results that show its potential for capturing multiple, existing or novel, recommendations easily and having a flexible recommendation system that combines extensibility with reasonable performance.

CourseRank is a course planning tool aimed at helping students at Stanford. Recommendations comprise an integral part of it. However, implementing existing recommendation methods leads to fixed recommendations that cannot adapt to each particular student's changing requirements and do not help exploit the full extent of the available learning opportunities at the university. In this paper, we describe the concept of a flexible recommendation workflow, i.e., a high-level description of a parameterized process for computing recommendations. The input parameters of a flexible recommendation process comprise the "knobs" that control the final output and hence generate flexible recommendations. We describe how flexible recommendations can be expressed over a relational database and we present our prototype system that allows defining and executing different, fully-parameterized, recommendation workflows over relational data. Finally, we describe a user interface in CourseRank that allows students customize recommendations.