An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

Margaris, Dionisis; Kobusińska, Anna; Spiliotopoulos, Dimitris; Vassilakis, Costas

doi:10.1109/access.2020.2981567

Cited by 29 publications

(7 citation statements)

References 23 publications

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“…To get a more accurate prediction, they apply a trust expansion strategy on the explicit trust relations to mine more neighbors for each user. Margaris et al [32] proposed an algorithm that finds two neighborhood sets for a target user. The first set of neighbors is formed by means of the social network, while the second set is formed by the rating matrix.…”

Section: B Neighborhood-based Approachesmentioning

confidence: 99%

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

Dadgar

Hamzeh

2022

IEEE Access

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With the increasing number of items in electronic retailers, news websites, etc., finding interesting items concerning the taste of users is becoming more challenging. Recommender Systems (RS) are a well-known solution to this issue. Collaborative filtering (CF) is a widely accepted and popular technique to implement an RS. However, cold-start and data sparsity problems reduce the performance of CF methods. One promising solution for these issues is to use the social trust information. However, how to properly use social trust information is a hot and still open question. In this paper, we propose a similarity measure and a simple link prediction method to address this question and employ them in trust-aware matrix factorization. Especially, our proposed similarity measure is asymmetric to consider the nature of social relationships. Also, to have a more accurate similarity estimation, we have considered both the user's historical ratings and trust relations, and we have determined the weight of each source. Finally, we have used the item-based model and the level of interest a user's trustee has for an item to improve the performance of the proposed method for sparse datasets. We conduct extensive performance evaluations in terms of rate prediction and interesting items found. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed method, especially in terms of Mean Absolute Error.

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Section: B Neighborhood-based Approachesmentioning

confidence: 99%

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

Dadgar

Hamzeh

2022

IEEE Access

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“…In this paper, we use a combination of trust and similarity to predict the score, our method is similar to [25], and the fusion of trust and similarity is shown in (11). The prediction is shown in (12).…”

Section: Score Predictionmentioning

confidence: 99%

Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Tong

2022

IEEE Access

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Trust recommendation is a vital recommendation system application based on social networks. It can recommend items based on the trust between users, which can alleviate data sparseness and enhance the interpretability of results. A large number of recommendation algorithms have been proposed, but most of them believe that trust is fixed, ignoring the changes of trust in the process of interaction. In addition, deep learning models are good at solving complex tasks and processing high-dimensional data, and they can model recommendation algorithms, but they are insufficient in capturing changes in user preferences timely. Therefore, given the shortcomings of existing researches, we propose a DDPG-TR algorithm based on deep reinforcement learning to capture the changes in user preferences and update the trust between users. The algorithm uses deep deterministic policy gradient algorithm DDPG to model the user-item interaction process. Firstly, we improve a state representative structure to express the user's state, which is convenient to capture the changes in user preferences. Then, when the user accepts the recommendation, algorithm combines trust and similar information to predict item score, as well as calculates the difference of the score. Finally, Agent gets the score feedback and uses the difference to update trust. Experiments are conducted on three datasets, and they are verified that the DDPG-TR algorithm can provide more accurate recommendation results, compared with other recommendation algorithms.

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“…As a commonly-used metric in NLP, one can leverage the cosine distance of two message embeddings (the embedding process can be either parametric or non-parametric) to quantize their similarity. Some typical examples can be found in text/image retrieval [43], image quality assessment [44], social network inference [45], etc. However, finding the most suitable embedding function is still an open question in this case.…”

Section: A From Bit-level Accuracy To Semantic Similaritymentioning

confidence: 99%

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

Lu¹,

Li²,

Chen³

et al. 2021

Preprint

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We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JSCC) methods, we show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. To address this problem, we take advantage of learning from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, to develop such a semantic communication system is indeed a nontrivial task, considering the nondifferentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement by using the policy gradient technique, and to interact with the surrounding noisy environment in a natural way. We have testified the proposed method in the challenging European-parliament dataset. Experiments on both AWGN and phase-invariant fading channel have confirmed the superiority of our method in revealing the semantic meanings, and better handling the channel noise especially in low-SNR situations. Apart from the experimental results, we further provide an indepth look at how the semantics model behaves, along with its superb generalization ability in real-life examples. As a brand new method in learning-based JSCC tasks, we also exemplify an RL-based image transmission paradigm, both to prove the generalization ability, and to leave this new topic for future discussion.

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An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

Cited by 29 publications

References 23 publications

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

How to Boost the Performance of Recommender Systems by Social Trust? Studying the Challenges and Proposing a Solution

Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

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