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AEC personalization framework for regulation retrieval
Published in Manuel Martínez, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2020
Collaborative filtering and recommendation systems. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting information about interests from many users (collaborating). Recommendation systems are programs which are often used in e-commerce services: they process a dynamic user’s profile to predict items he may be interested in. Often, they are implemented as collaborative filtering algorithms. Hence, predictions are specific to a user, but use information gleaned from many users. Filtering techniques can be usefully applied to very few categories where a single person can make relevant choices. Many commercial web sites adopt collaborative filtering techniques (i.e. Amazon.com “users who bought also bought”), or enable predictive modeling and other artificial intelligence techniques.
Movie Recommendations Based on a Recurrent Neural Network Model
Published in Stuart H Rubin, Lydia Bouzar-Benlabiod, Reuse in Intelligent Systems, 2020
Collaborative filtering is an approach for recommendation systems which relies on the ratings for a particular user as well as the ratings of similar users. The underlying assumption is that if we can accurately predict movie ratings, then we can recommend new movies to users that they are likely to enjoy, including movies the user may not have considered before. Therefore, in the context of movie recommendation, collaborative filtering aims to predict unknown movie ratings for a particular user, based on that user’s known ratings as well as the movie ratings by other users in the system. As opposed to content-based systems, collaborative filtering accounts for users with diverse taste, so long as there are other users with similar preferences. By finding similar users, new items can be recommended based on the assumption that items which are liked by similar users will be liked by the user in question.
Used Products Return Service Based on Ambient Recommender Systems to Promote Sustainable Choices
Published in Qurban A. Memon, Distributed Networks, 2017
Gao Wen-Jing, Bo Xing, Tshilidzi Marwala
Recommender systems are designed to make recommendations (typical products and ser-vices) to users of the Internet based on prior user actions and a model of user preferences. Often, this model is derived from cross-similarities among activity profiles across a collection of users, in which case it is termed collaborative filtering. A familiar example of collaborative filtering is Amazon.com’s ‘customers who bought’ feature. In addition, the authors of Ref. [34] suggested the use of recommendation agents as a promising approach in future e-learning systems, where a recommender agent saw what a student was doing and recommended actions. Meanwhile, the recommender system can be used to provide the user with access to political documents and to political information according to the user profile [35].
Intelligent recommendation model of tourist places based on collaborative filtering and user preferences
Published in Applied Artificial Intelligence, 2023
The collaborative filtering algorithm is mainly used to predict the target users’ evaluation of similar products by analyzing the users’ evaluation of some products, and use it as the basis for making recommendations to users. The collaborative filtering algorithm is a popular and widely used approach in recommendation systems, as it is based on the assumption that users who have similar preferences in the past are likely to have similar preferences in the future. Collaborative filtering can be used to make personalized recommendations by identifying users with similar preferences and recommending items that similar users have liked in the past. In the context of tourism recommendation systems, it is important to provide personalized recommendations tailored to each user’s interests and preferences. However, traditional collaborative filtering algorithms may not perform well when there is sparse data or when users have diverse preferences that are not captured by the similarity measures used in the algorithm.
Research on social recommendation algorithm based on fuzzy subjective trust
Published in Connection Science, 2022
In recent years, with the rapid development of social networks such as Facebook, Twitter, Weibo, WeChat, QQ, etc., various social applications and services have penetrated into many fields such as instant messaging, e-commerce and consumer recommendation. Social networking has become one of the main ways for people to communicate and obtain information (Arora & Taneja, 2021; Meeker, 2017; Shen et al., 2020). As the main body of the social network platform, users have established complex and diverse social networks based on their actual needs, real social relationships and activities. On social networks, people are no longer satisfied to communicate with friends they know in the real world, but hope that social networks can recommend them more new users with common interests and hobbies, continue to expand their social relationships and learn more useful information. How to provide users with service recommendations safely, quickly and accurately has become one of the hotspots of social network service research (Bushra & Yousef, 2020). Traditional recommendation systems are mostly based on collaborative filtering algorithms (Liu et al., 2020), content data filtering algorithms, and hybrid recommendation algorithms (Bushra & Yousef, 2020; Zhang, 2019; Zhu, 2020). Among them, the collaborative filtering algorithm is one of the most widely used personalised recommendation algorithms, but the algorithm has problems such as cold start (Li et al., 2022), data sparseness, forgery of scoring data, and inaccurate recommendation accuracy (Chen et al., 2016; Jia et al., 2019; Nassira et al., 2020; Yu, Chen, et al., 2020).
Toward Intelligent Recommendations Using the Neural Knowledge DNA
Published in Cybernetics and Systems, 2021
Guangjian Ning, Chunwang Wu, Yuan Li, Haoxi Zhang, Edward Szczerbicki
Recently, researchers have developed various approaches for creating recommendation systems. Based on the key technologies they are built with, recommendation systems can be broadly classified into three categories, namely Collaborative Filtering (Acilar and Arslan 2009), content-based recommending (Chen et al. 2008), and Hybrid approaches (Jalali et al. 2010):Collaborative Filtering: In collaborative filtering systems, the user is recommended items that people with similar tastes and preferences liked in the past;Content-based recommending: In these approaches, the user is recommended items similar to the ones the user preferred in the past;Hybrid approaches: These methods combine collaborative and content-based methods.