Overcoming Data Sparsity in Group Recommendation
نویسندگان
چکیده
منابع مشابه
Improving Sparsity Problem in Group Recommendation
Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group satisfaction and lower group dissatisf...
متن کاملRecommendation by Mining Multiple User Behaviors with Group Sparsity
Recently, some recommendation methods try to improve the prediction results by integrating information from user’s multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behavi...
متن کاملOvercoming Vocabulary Sparsity in MT Using Lattices
Source languages with complex wordformation rules present a challenge for statistical machine translation (SMT). In this paper, we take on three facets of this challenge: (1) common stems are fragmented into many different forms in training data, (2) rare and unknown words are frequent in test data, and (3) spelling variation creates additional sparseness problems. We present a novel, lightweig...
متن کاملOvercoming Data Sparsity & Bias in Order to Recommend from the “ Long Tail ”
Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased item...
متن کاملJoint Variable Selection for Data Envelopment Analysis via Group Sparsity
This study develops a data-driven group variable selection method for data envelopment analysis (DEA), a non-parametric linear programming approach to the estimation of production frontiers. The proposed method extends the group Lasso (least absolute shrinkage and selection operator) designed for variable selection on (often predefined) groups of variables in linear regression models to DEA mod...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2020
ISSN: 1041-4347,1558-2191,2326-3865
DOI: 10.1109/tkde.2020.3023787