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Making Flexible Recommendations in CourseRank (Posted by Georgia Koutrika)

Some background on CourseRank

CourseRank is a social tool for course planning that we are developing at the InfoLab. CourseRank helps Stanford students make informed decisions about classes. It displays official university information and statistics, such as bulletin course descriptions, grade distributions, and results of official course evaluations. Students can anonymously rank courses they have taken, add comments, and rank the accuracy of each others' comments. They can also shop for classes, get personalized recommendations, and organize their classes into a quarterly schedule or devise a four-year plan.

CourseRank also functions as a feedback tool for faculty and administrators, ensuring that information is as accurate as possible. Faculty can modify or add comments to their courses, and can see how their class compares to other classes.

A year after its launch, the system is already being used by more than 6,200 Stanford students, out of a total of about 14,000 students. The vast majority of CourseRank users are undergraduates, and there are only about 7,000 undergraduates at Stanford. Thus, CourseRank is already used by a very large fraction of Stanford undergraduates.

Recommendations the Classical Way

Recommendations comprise an integral part of the system functionality. In order to generate recommendations for students, we initially adopted existing recommendation approaches (see Adomavicius and Tuzhilin for a good survey). However, although the initial version of CourseRank has been very popular with students (see editorial in the Stanford student paper), we soon had first-hand experience with the limitations of classical recommendation systems.

Limitation: Inflexible, canned recommendations

Similarly to most commercial recommendation systems, our initial version offered no choices, just a fixed list of recommended courses for the student to consider. However, users may expect different recommendations under different circumstances, and we received many requests, from students and administrators, for more flexible recommendations. For example, students want to specify the type of course they are interested in (e.g., a biology class, something that satisfies Stanford's writing requirement). They also want to request recommendations based on a peer group they select (e.g., students in their major, or freshmen only) and based on different criteria (e.g., students in their major with similar grades or with similar ratings). For example, a student may want recommendations for CS courses from CS students with similar grades (i.e., with similar performance) and for dance classes from students with similar ratings (i.e., with similar tastes). In some cases, students want to see the recommendations the system would give their best friend, not themselves.

Limitation: Hard-wired recommendations

The recommendation algorithm is typically embedded in the system code, not expressed declaratively. From the designer viewpoint, this fact makes it hard to modify the algorithm, or to experiment with different approaches. However, we (the CourseRank implementers) want to experiment with different recommendation approaches: Would approach X be more effective than approach Y in our environment? What are the right weights for blending two recommendations (e.g., one based on what students like, and another based on what courses are more critical for completing a major)? What is the best way to predict, not good courses, but likely grades a student will obtain in future courses?

To accommodate our end-users’ requests and also to meet our own research and development goals, we were faced with the prospects of implementing many different recommendation approaches and their variants. Therefore, we decided we needed to have more flexible recommendations, i.e., recommendations that could be easily described, customized, and executed by a single engine.

The idea of Flexible Recommendations (FlexRecs)

So, we designed FlexRecs, a framework for flexible recommendations over relational data. The general idea of FlexRecs can be described as follows:
  • A given recommendation approach is expressed declaratively as a high-level workflow. Imagine that such a workflow comprises a series of interconnected operators that describe how a recommendation is computed. In a workflow, sets of objects flow from one operator to the next, and are filtered, ranked, etc. in the process. The workflow is a “high-level” description in the sense that it does not contain actual code, but rather, it describes what code (operators) to call upon. These descriptions can also handle parameters, so that end-users can at run time personalize their recommendations, e.g., by choosing the peer group of students to provide recommendations, or by weighting recommendations that are blended.
  • A designer can easily express multiple workflows for different types of recommendations, as well as new types that the designer may think of. The end user can select from them, depending on her information needs. This selection is done through a GUI, which also allows the user to enter parameters for workflows in order to get more accurate and personalized recommendations. For instance, the user may specify that her recommendations be based on what courses students in her major are taking. Such functionality is essentially similar to advanced searches: a designer builds a set of parameterized queries. End users can transparently execute these queries with parameter values and receive different results through an advanced search interface.
Our framework describes how a recommendation workflow can specifically be defined for relational data, using traditional relational operators such as select, project and join, plus new recommendation operators that generate or combine recommendations. The recommendation operators may call upon functions in a library that implement common tasks for generating recommendations, such as computing the Jaccard or Pearson similarity of two sets of objects. The system can execute a workflow by "compiling" it into a sequence of SQL calls, which are executed by a conventional DBMS. When possible, library functions are compiled into the SQL statements themselves; in other cases we can rely on external functions that are called by the SQL statements.

We have built a flexible recommendation engine for compiling and executing FlexRecs workflows as part of CourseRank. The FlexRecs framework has indeed made it possible to define many recommendation strategies (at least all of the ones we have been able to think of to date). The new version of CourseRank, to be released September 2008, will let end-users tailor their recommendations. Although the end-users will only see choices from simple menus, and select values from sliders, they will actually be selecting what workflow to run, and with what parameters.

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