FeRe: Exploiting influence of multi-dimensional features resided in news domain for recommendation

Lv, Pengtao; Meng, Xiangwu; Zhang, Yujie

doi:10.1016/j.ipm.2017.04.008

Cited by 28 publications

(7 citation statements)

References 18 publications

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“…Recall and NDCG are popular metrics for evaluating recommender systems and have been widely used to measure the effectiveness of group recommendation [10], [11], [26]. We do not adopt Precision to evaluate our model, because Precision is defined as the proportion of relevant events in recommendation lists and misses unknown positives [28], [29]. Specifically, Recall@k and NDCG@k, which measure the Recall and NDCG of a group event recommendation method that produces a list of k events for each group respectively, are computed as follows.…”

Section: ) Evaluation Methodsmentioning

confidence: 99%

Hybrid Deep Framework for Group Event Recommendation

Zhu

et al. 2020

IEEE Access

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Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed. However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users. In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation. Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM. The former extracts high latent group preference from user feedback and group feedback. The latter obtains latent event features based on contextual information, such as location and organizer of events. Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem. We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively. INDEX TERMS Group recommendation, hybrid recommendation, cold-start problem, restricted Boltzmann machines, deep belief networks.

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Section: ) Evaluation Methodsmentioning

confidence: 99%

Hybrid Deep Framework for Group Event Recommendation

Zhu

et al. 2020

IEEE Access

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“…Features with poor performance will greatly affect the effects of early fusion [36]. In contrast to early fusion, late fusion uses mechanisms such as averaging [37], voting [38], and learned model [39,40] to fuse predictions from each model. In our paper, we aimed to compare the effects of early and late fusions in user preference prediction.…”

Section: Related Workmentioning

confidence: 99%

Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

Wang

2021

Applied Sciences

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In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

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“…Over the past years, the principal research about the social network focuses on the effect of social data analysis [9][10][11][12] . With the joint efforts of scholars in the world, the semantics analysis method represented by Biterm topic model (BTM) [2] has been well studied and has been widely used in the information searching and recommendation field during the last years [13][14][15] . Nevertheless, traditional models cannot be used directly to detect topics in the data stream.…”

Section: Related Workmentioning

confidence: 99%