The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

Following the advances in various smart devices, there are increasing numbers of users of social network services (SNS), which allows communication and information sharing in real time without limitations on distance or space. Although personal information leakage can occur through SNS, where an individual's personal details or online activities are leaked, and various financial crimes such as phishing and smishing are also possible, there are currently no countermeasures. Consequently, malicious activities are being conducted through messages toward the users who are in follow or friend relationships on SNS. Therefore, in this paper, we propose a method of assessing follow suggestions from users with less likelihood of committing malicious activities through an information-driven follow suggestion based on a categorical classification of interests using both the images and text of user posts. We ensure the objectiveness of interest categories by defining these based on DMOZ, which is established by the Open Directory Project. The images and text are learnt using a convolutional neural network, which is one of the machine learning techniques developed with a biological inspiration, and the interests are classified into categories. Users with a large number of posts are defined as certified users, and a database of certified users is established. Users with similar interests are classified, and the similarity distances between certified users and users are measured, and a follow suggestion is generated to the certified user with the most similar interest. Using the method proposed in this paper to classify the interest categories of certified users and users, precisions of 80% and 79.8% were obtained, respectively, and the overall precision was 79.93%, indicating a good classification performance overall. It is expected that the method proposed in this paper can be used to provide follow suggestions of users with less likelihood of malicious activities based on the information posted by the user.

No abstract

The most typical problem in an analysis of natural language is finding synonyms of out-of-vocabulary (OOV) words. When someone tries to understand a sentence containing an OOV word, the person determines the most appropriate meaning of a replacement word using the meanings of co-occurrence words under the same context based on the conceptual system learned. In this study, a word-to-vector and conceptual relationship (Word2VnCR) algorithm is proposed that replaces an OOV word leading to an erroneous morphemic analysis with an appropriate synonym. TheWord2VnCR algorithm is an improvement over the conventional Word2Vec algorithm, which has a problem in suggesting a replacement word by not determining the similarity of the word. After word-embedding learning is conducted using the learning dataset, the replacement word candidates of the OOV word are extracted. The semantic similarities of the extracted replacement word candidates are measured with the surrounding neighboring words of the OOV word, and a replacement word having the highest similarity value is selected as a replacement. To evaluate the performance of the proposed Word2VnCR algorithm, a comparative experiment was conducted using the Word2VnCR and Word2Vec algorithms. As the experimental results indicate, the proposed algorithm shows a higher accuracy than the Word2Vec algorithm.

No abstract