FUIR: Fusing user and item information to deal with data sparsity by using side information in recommendation systems

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

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A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.36 ◽

2021 ◽

Vol 14 (1) ◽

pp. 387-399

Author(s):

Noor Ifada ◽

◽

Richi Nayak ◽

Keyword(s):

Recommendation System ◽

Recommendation Systems ◽

Learning Approach ◽

Tensor Model ◽

Learning Approaches ◽

Data Sparsity ◽

Tensor Models ◽

Sparsity Problem ◽

Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

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Learning Discriminative Recommendation Systems with Side Information

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/485 ◽

2017 ◽

Cited By ~ 3

Author(s):

Feipeng Zhao ◽

Yuhong Guo

Keyword(s):

Linear Regression ◽

Prediction Model ◽

Regression Model ◽

Real World ◽

Linear Regression Model ◽

Side Information ◽

Recommendation Systems ◽

Joint Model ◽

Partially Observed ◽

Real World Applications

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.

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Improvement of Data Sparsity and Scalability Problems in Collaborative Filtering Based Recommendation Systems

Applied Computing and Information Technology - Studies in Computational Intelligence ◽

10.1007/978-3-030-25217-5_2 ◽

2019 ◽

pp. 17-31

Author(s):

Ji-Won Choi ◽

Sang-Kweon Yun ◽

Jong-Bae Kim

Keyword(s):

Collaborative Filtering ◽

Recommendation Systems ◽

Data Sparsity

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Collaborative Filtering: Data Sparsity Challenges

International Journal of Multidisciplinary and Current Research ◽

10.14741/ijmcr/v.6.6.13 ◽

2018 ◽

Vol 6 (6) ◽

Author(s):

Er.Meenakshi . ◽

Dr.Satpal .

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Effective Means ◽

Recommendation Systems ◽

Decision Making Process ◽

Huge Amount ◽

Data Sparsity ◽

Filtering Technique ◽

The Cost

Today internet is a place where the huge amount of data is stored, there is need to sift, which create a problem for the internet user, so recommend system solve the problem. A recommendation system is a system that helps a user found the products and content by forecast the user’s rating of each item and showing them the items that they would rate highly. Recommendation systems are everywhere. With online shopping, customer has nearly infinite choices. No one has enough time to try every product for sale. Recommendation systems play an important role to solve the users search the products and content they care about. Recommendation system is a process of filtering the information that deal with information overloaded problems. Recommendation system is important for both user and service provider. It reduces the cost of transaction and selecting item in an online scenario it also improve the quality of decision making process. It is now an effective means for selling their product. So over emphasized of user is not good for recommendation system. To solve the problems of recommendation system like data sparsity we use one of best technique that is collaborative filtering technique.

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