Normalizing Item‐Based Collaborative Filter Using Context‐Aware Scaled Baseline Predictor

Ma, Wenming; Shi, Juntai; Zhao, Renda

doi:10.1155/2017/6562371

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Where N represents the number of recommendations to the user. The various RSs (Goldberg et al [31], Ma et al [55]) in the past have used this measure for their assessments.…”

Section: Root Of Mean Square Error (Rmse)mentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

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“…Where N represents the number of recommendations to the user. The various RSs (Goldberg et al [31], Ma et al [55]) in the past have used this measure for their assessments.…”

Section: Root Of Mean Square Error (Rmse)mentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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“…User-Item Bias Shifting. Bias occurs when some users are more inclined to give higher rating values compare to others who tend to give lower ratings, or when some items receive high rating values compare to others [33]. For instance, a user who tends to give an average movie 4-stars ratings may be inclined to now give such movie a 3-stars rating.…”

Section: Types Of Concept Drifts Incorporated In Drssmentioning

confidence: 99%

“…For instance, a user who tends to give an average movie 4-stars ratings may be inclined to now give such movie a 3-stars rating. Different users in the events of multi-person accounts also tend to give different ratings for similar items [5,33]. Other factors include the ratings given in relation to other ratings previously given by others.…”

Section: Types Of Concept Drifts Incorporated In Drssmentioning

confidence: 99%

Recommender System Based on Temporal Models: A Systematic Review

et al. 2020

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Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.

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“…The item-based collaborative filtering algorithm mainly includes: (1) using the existing user item rating record to calculate the similarity between items;(2) finding the nearest neighbor set of the target item according to the size of the similarity; (3) predicting the target item's rating by using the target user's rating of the nearest neighbor set item, and recommending the item to the user with a high forecast rating [15,16,17]. In order to alleviate data sparseness and scalability problem of the user-based collaborative filtering recommendation algorithm, it chooses to use Slope one algorithm [18].…”

Section: Item-based Collaborative Filtering Algorithmmentioning

confidence: 99%

Field Information Recommendation Based on Context-Aware and Collaborative Filtering Algorithm

Chen

Zhao

2019

Computer and Computing Technologies in Agriculture XI

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Personalized recommendation technology is a valid way to solve the problem of "information overload". In the face the complexity of agricultural field information and problems of farmers' preference prediction accuracy which is not high, this paper proposes a kind of recommendation algorithm based on context-aware and collaborative filtering(CACF). The algorithm constructs the "user-item-context" 3D user interest model which contains the context information. Through calculating context similarity and adopting pre-filtering paradigm, the 3D model is reduced to "user-item"2D model. By computing item similarity, it can predict the item rating and generate recommendations. The CACF was applied on the field information recommendation. The experimental results show that the CACF can accomplish higher recommendation precision and efficiency compared with the traditional User-based collaborative filtering algorithm(UBCF), Slope one algorithm(SLOA).

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Normalizing Item‐Based Collaborative Filter Using Context‐Aware Scaled Baseline Predictor

Cited by 6 publications

References 23 publications

Assessment Methods for Evaluation of Recommender Systems: A Survey

Assessment Methods for Evaluation of Recommender Systems: A Survey

Recommender System Based on Temporal Models: A Systematic Review

Field Information Recommendation Based on Context-Aware and Collaborative Filtering Algorithm

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