Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Romadhon, Zainur; Sediyono, Eko; Widodo, Catur Edi

doi:10.22219/kinetik.v5i3.1062

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Cosine similarity can be used to provide recommendations for users based on their preferences. [30].…”

Section: Recommendation System Resultsmentioning

confidence: 99%

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

Salsabil,

Setiawan,

Kurniawan

2024

KINETIK

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The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.

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“…Cosine similarity can be used to provide recommendations for users based on their preferences. [30].…”

Section: Recommendation System Resultsmentioning

confidence: 99%

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

Salsabil,

Setiawan,

Kurniawan

2024

KINETIK

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“…e excellent algorithm not only requires stable and accurate operationbut also must contact the application environment with a certain universality, so it can meet the needs of the user key to see the effective use of the algorithm, because the method recommended by the algorithm will be different, which requires researchers to study rational selection through the experiment and targeted trade-off for the field. After 20 years of development of the recommendation system, scholars have made use of knowledge in different fields to improve the recommendation algorithm from multiple perspectives and put forward different recommendation algorithms [18]. At present, the recognized recommendation algorithms include collaborative filtering recommendation, content-based recommendation, knowledge-based recommendation, and hybrid recommendation algorithm [19].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

Zhang

Zhang³

2021

Scientific Programming

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This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filtering. When the number of users and evaluation levels are large, the user rating data matrix of collaborative filtering prediction will become relatively dense, which can reduce the sparsity of the matrix and make collaborative filtering more accurate. In this way, the system performance will be greatly improved through the integration of the two. On the basis of the improved collaborative filtering algorithm, a hybrid algorithm based on content and improved collaborative filtering was proposed. By combining user rating with item features, a user feature rating matrix was established to replace the traditional user-item rating matrix. K-means clustering was performed on the user set and recommendations were made. The improved algorithm can solve the problem of data sparsity of traditional collaborative filtering algorithm. At the same time, for new projects, it can also predict users who may be interested in new projects according to the matching of project characteristics and user characteristics scoring matrix and generate push list, which effectively solve the problem of new projects in “cold start.” The experimental results show that the improved algorithm in this paper plays a significant role in solving the speed bottleneck problems of data sparsity, cold start, and online recommendation and can ensure a better recommendation quality.

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“…For example, research examines the design of food sharing mobile apps [27] and webpages [28]. Particularly, research proposes food sharing ICTs [29], designs P2P food sharing networks [18], or even develops recommendation systems for P2P collaboration [30]. Behavioral research, on the other hand, examines how digital technology mediates the behavior of consumers in various online systems that facilitate offline gift giving and sharing [31].…”

Section: Sharing Uneaten Food With Others Via P2p Social Platformsmentioning

confidence: 99%

Sharing Leftover Food with Strangers via Social Media: A Value Perspective Based on Beliefs-Values-Behavior Framework

2021

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An emergent effort to reduce wasted food is to share uneaten food with others via social media. However, the following question arises: Are those unknown people willing to take my leftovers? Few studies address the above question. Hence, developing a comprehensive model that explains the acceptance of leftover food from strangers (LFFS) via social media warrants research attention. Considering the dual roles LFFS recipients play—namely, both peer-to-peer (P2P) technology user and service consumer—the study embraces diverse motivational factors across research disciplines to explain LFFS acceptance. Drawing on technology and marketing research, the study develops a value-based model to investigate consumer acceptance of LFFS via social media. The study examines the effects of two prominent consumers’ beliefs (cognitive interaction and affective trust) on their context-specific value perception (conditional and epistemic values) toward LFFS, and the impact of these perceived values on the acceptance of the leftover food from strangers. The study employed a two-stage data collection approach and collected 663 usable questionnaire packets from two major metropolitan areas in Taiwan. Using a Structural Equation Model (IBM SPSS Amos) to analyze the data, the results indicate that trust and interactivity relate positively to consumer perceived value (both conditional and epistemic) of LFFS. Furthermore, both conditional and epistemic values mediate the relationships between the proposed consumer beliefs and LFFS acceptance. The research helps create a sustainable society as sharing uneaten food with other unknown social community members provides a connected, diverse, and sustainable life.

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Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Cited by 7 publications

References 17 publications

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation

Sharing Leftover Food with Strangers via Social Media: A Value Perspective Based on Beliefs-Values-Behavior Framework

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