A fuzzy tree similarity based recommendation approach for telecom products

Wu, Dianshuang; Zhang, Guangquan; Lu, Jie

doi:10.1109/ifsa-nafips.2013.6608505

Cited by 4 publications

(2 citation statements)

References 19 publications

(20 reference statements)

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“…Herein, the behaviors of users based on the data collected in the past are analyzed to determine the connection between users and their items of interest, which can aid in recommending items to the users with similar preferences and support considering the opinions of different users. A hybrid recommendation approach that combines user-based and item-based CF techniques was proposed for mobile product and service recommendation [28][29][30] . Although CF can achieve efficiency based on similar measurements of users' interests and recommended items, it is difficult to exploit the cross features completely owing to the lack of deep and effective feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Recommender System for Telecom Packages Based on the Deep & Cross Network

Shi

Wen

Wei

et al. 2021

Preprint

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With the evolution of 5G technology, the telecom industry has influenced the livelihood of people and impacted the development of the national economy significantly. To increase revenue per customer and secure long-term contracts of users, telecommunications firms and enterprises have launched different types of telecom packages to satisfy the varying requirements of users. Although several recommender systems have been proposed in recent years for telecommunication package recommendation, extracting effective feature information from large and complex consumption data remains challenging. Considering the telecom package recommendation problems, traditional recommendation methods either use complex expert feature engineering or fail to perform end-to-end deep learning training. In this study, a recommender system based on deep & cross network (DCN), deep belief network (DBN), Embedding, and Word2Vec is proposed using the powerful learning abilities of deep learning. The proposed system fits the telecom package recommender system in terms of click-through rate prediction to provide a potential solution for the recommendation challenges faced by telecom enterprises. The proposed model can effectively capture the finite order interactional features and deep hidden features. Additionally, the text information in the data is completely used to further improve the recommendation ability of the model. Moreover, the proposed method does not require feature engineering. We conducted comprehensive experiments using real-world datasets, and the results verify that our method can generate improved recommendation accuracy in comparison with those observed in DBN, DCN, deep factorization machine, and deep neural network models individually.

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Section: Related Workmentioning

confidence: 99%

Recommender System for Telecom Packages Based on the Deep & Cross Network

Shi

Wen

Wei

et al. 2021

Preprint

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“…They apply two fuzzy inputs: "degree of homophily" (low/medium/high) and "degree of separation" (close/medium/far) between users and use a set of decision rules to determine a level of trustworthiness based on these fuzzy inputs. Wu et al 30,47 discuss a recommendation algorithm for telecom products employing a hybrid of item-and user-based collaborative filtering. They propose a similarity measure based on trees 31 with fuzzy numbers applied to model uncertain weights and linguistic variables.…”

Section: Fuzzy Approaches To the Recommendation Problemmentioning

confidence: 99%

A Fuzzy Recommender System for Public Library Catalogs

Morawski

Stepan

Dick

et al. 2017

Int. J. Intell. Syst.

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Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton‐area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative‐filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold‐start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.

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