Recommendation Algorithms for E-Commerce and Education

Major Research Products 

Sparse linear methods for top-n recommender systems

Xia Ning and George Karypis. SLIM: Sparse linear methods for top-n recommender systems. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM’11, pages 497–506, Dec 2011.

Abstract: This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an ℓ1 -norm and ℓ2 -norm regularized optimization problem. W is demonstrated to produce high quality recommendations and its sparsity allows SLIM to generate recommendations very fast. A comprehensive set of experiments is conducted by comparing the SLIM method and other state-of-the-art top-N recommendation methods. The experiments show that SLIM achieves significant improvements both in run time performance and recommendation quality over the best existing methods.

The SLIM code is available here and here. SLIM has been used by Mendeley, Alibaba, Ebay, etc., and has been re-implemented in R, Python, C# and Java, and included in various libraries (librec, mymedialite,  mrec) and textbooks. The SLIM paper won the 10-Years-Highest-Impact-Paper Award, IEEE International Conference on Data Mining (ICDM) 2020.

Sequential recommendation

Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, and Xia Ning. HAM: Hybrid associations model with pooling for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3049692.

Abstract: Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user’s purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations using three factors: 1) users long-term preferences, 2) sequential, high-order and low-order association patterns in the users most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings, with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods, and are able to achieve significant speedup as much as 139.7 folds.

Code is available here.

Recommendation algorithms for educational data mining

Zhiyun Ren, Xia Ning, and Huzefa Rangwala. Grade prediction with temporal course-wise influence. In Proceedings of the 10th International Confernece on Educational Data Mining, EDM’17, pages 48–56, 2017.

Abstract:  There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective elds of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal eects for grade prediction. In this model, students and courses are represented in a latent “knowledge” space. The grade of a student on a course is modeled as the similarity of their latent representation in the “knowledge” space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.