A Collaborative Filtering Method for Web Data Service in Cloud Environment

In this paper detection method for the illegal access to the cloud infrastructure is proposed. Detection process is based on the collaborative filtering algorithm constructed on the cloud model. Here, first of all, the normal behavior of the user is formed in the shape of a cloud model, then these models are compared with each other by using the cosine similarity method and by applying the collaborative filtering method the deviations from the normal behavior are evaluated. If the deviation value is above than the threshold, the user who gained access to the system is evaluated as illegal, otherwise he is evaluated as a real user.

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A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

Applied Sciences ◽

10.3390/app11125416 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5416

Author(s):

Yanheng Liu ◽

Minghao Yin ◽

Xu Zhou

Keyword(s):

Collaborative Filtering ◽

User Preference ◽

Computation Method ◽

Group Recommendation ◽

Preference Rating ◽

Filtering Method ◽

Personal Recommendation ◽

Collaborative Filtering Algorithm ◽

Group Members ◽

Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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A Book Recommender System Using Collaborative Filtering Method

International Conference on Data Science, E-learning and Information Systems 2021 ◽

10.1145/3460620.3460744 ◽

2021 ◽

Author(s):

Sewar Khalifeh ◽

Amjed A. Al-Mousa

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Filtering Method

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Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i04.p01 ◽

2020 ◽

Vol 8 (4) ◽

pp. 367

Author(s):

Muhammad Arief Budiman ◽

Gst. Ayu Vida Mastrika Giri

Keyword(s):

Collaborative Filtering ◽

Mobile Phones ◽

Recommendation System ◽

Nearest Neighbor ◽

Cosine Similarity ◽

Music Recommendation ◽

K Nearest Neighbor ◽

Filtering Method ◽

K Nearest Neighbor Algorithm ◽

Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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