Online Transaction Shopping Items Basket Recommender Systems
Issue:
Volume 7, Issue 2, December 2022
Pages:
15-17
Received:
12 July 2022
Accepted:
28 July 2022
Published:
25 August 2022
DOI:
10.11648/j.mlr.20220702.11
Downloads:
Views:
Abstract: Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.
Abstract: Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular ...
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Automatic Indexing of Digital Objects Through Learning from User Data
Issue:
Volume 7, Issue 2, December 2022
Pages:
18-23
Received:
17 December 2022
Accepted:
12 January 2023
Published:
31 January 2023
Abstract: Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.
Abstract: Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repos...
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