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Enhancing LLM-based Zero-Shot Conversational Recommendation via Reasoning Path
Heejin Kook, Seongmin Park, Jongwuk Lee
http://doi.org/10.5626/JOK.2025.52.7.617
Conversational recommender systems provide personalized recommendations through bi-directional interactions with users. Traditional conversational recommender systems rely on external knowledge, such as knowledge graphs, to effectively capture user preferences. While recent rapid advancement of large language models has enabled zero-shot recommendations, challenges remain in understanding users' implicit preferences and designing optimal reasoning paths. To address these limitations, this study investigates the importance of appropriate reasoning path construction in zero-shot based conversational recommender systems and explores the potential of using a new approach based on this foundation. The proposed framework consists of two stages: (1) comprehensively extracting both explicit and implicit preferences from conversational context, and (2) constructing reasoning trees to select optimal reasoning paths based on these preferences. Experimental results on benchmark datasets INSPIRED and ReDial show that our proposed method achieves up to 11.77% improvement in Recall@10 compared to existing zero-shot methods, It even outperforms some learning-based models.
An Inference Framework for Text-Based Sequential Recommendation Model Using Nearest Neighbor Mechanism
Junyoung Kim, Hyunsoo, Jongwuk Lee
http://doi.org/10.5626/JOK.2025.52.5.435
Sequential recommendation task aims to predict the next item to interact with based on users’ interaction history. Text-based recommendation models, which represent items as text, show improved performance in cold-start problems and zero-shot recommendation tasks. However, they suffer from textual bias and the lack of collaborative knowledge. To overcome these limitations, we propose a text-based recommendation model inference framework using the nearest neighbor mechanism. The proposed method leverages text-based recommendation models as a neighbor retriever model to search neighbors with similar preferences to the user and aggregate the neighbor information with existing recommendation results to improve recommendation performance. Experiments conducted on four datasets show that the proposed method consistently outperforms existing models, with performance improvement up to 25.27% on NDCG@50. Furthermore, the proposed method effectively complements collaborative knowledge and improves model explainability by providing recommendation rationale.
Optimizing Computation of Tensor-Train Decomposed Embedding Layer
Seungmin Yu, Hayun Lee, Dongkun Shin
http://doi.org/10.5626/JOK.2023.50.9.729
Personalized recommendation system is ubiquitous in daily life. However, the huge amount of memory requirement to store the embedding tables used by deep learning-based recommendation system models is taking up most of the resources of industrial AI data centers. To overcome this problem, one of the solutions is to use Tensor-Train (TT) decomposition, is promising compression technique in deep neural network. In this study, we analyze unnecessary computations in Tensor-Train Gather and Reduce (TT-GnR) which is the operation of embedding layer applied with TT decomposition. To solve this problem, we define a computational unit called group to bind the item vectors into a group and propose Group Reduced TT-Gather and Reduce operation to reduce unnecessary operations by calculating with groups. Since the GRT-GnR operation is calculated in groups, computational cost varies depending on how item vectors are grouped. Experimental results showed that the GRT-GnR operation had a 41% decrease in latency compared to conventional TT-GnR operation.
Review-based Personalized Recommendation System using Effective Personalized Fusion and BERT
http://doi.org/10.5626/JOK.2023.50.8.646
Generally, review texts contain personal information from users, and reviews written by users can have different meanings, even if they use the exact wording. These review features can be used to compensate for the shortcomings of collaborative filtering, which is vulnerable to data sparsity. They can also be used as information for personalized recommendation systems. Despite the success of pre-trained language models in natural language processing, there has been little research on personalized recommendation systems that leverage BERT to enrich individual user features from reviews. In this work, we propose a rating prediction model that uses BERT for detailed learning of user and item-specific features from reviews and tightly combine them with user and product IDs to represent personalized user and item. Experiments results show that the proposed model can achieve improved performance over the baseline on the Amazon benchmark dataset.
Graph Embedding-Based Point-Of-Interest Recommendation Considering Weather Features
Kun Woo Lee, Jongseon Kim, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.3.221
As the Location-Based Services (LBS) grow rapidly, the Point-Of-Interest (POI) recommendation becomes an active research area to provide users appropriate information relevant to their locations. Recently, translation-based recommendation systems using graph embedding, such as TransRec, are attracting great attention. In this paper, we discovered some drawbacks of TransRec; it is limited in expressing the complex relationship between users and POIs, and the relation embedding is fixed without considering weather features. We propose WAPTRec, a graph embedding-based POI recommendation method considering the weather, that overcomes the drawback of TransRec. WAPTRec can rep resent the same POI embedding in different ways according to users by using a category projection matrix and attention mechanism. In addition, it provides better recommendation accuracy by utilizing the users’ movement history, category of POIs and weather features. Experiments using public datasets illustrated that WAPTRec outperformed the conventional translation-based recommendation methods.
Improving Performance of Recurrent Neural Network based Recommendations by Utilizing Personal Preferences
Dong Shin Lim, Yong Jun Yang, Shin Cho
http://doi.org/10.5626/JOK.2021.48.11.1211
As the amount of content provided on the platform surged, a recommendation system became an essential element of the platform. The collaborative filtering technique is a widely used recommendation system in academia and industry, but it also has a limitation because it relies on quantitative information from consumers such as ratings and purchase history. To overcome this shortcoming, various studies have been done in a bid to improve its performance by collecting qualitative information such as review data in a model. Recently, some studies that applied recurrent neural networks showed better performance than the existing recommendation system by using time-series behavioral data only, but studies that reflect each customer"s preference in the recommendation model have not yet been made. In this paper, an improved recommendation model was presented by calculating a preference matrix based on customer log data and learning it in a recurrent neural network through an embedding vector. It was confirmed that the prediction performance was improved compared to the existing recurrent neural network recommendation model.
A Weight-based Multi-domain Recommendation System for Alleviating the Cold-Start Problem
http://doi.org/10.5626/JOK.2021.48.10.1090
A recommendation system predicts users’ ratings based on users’ past behaviors and item preferences. One of the most famous types of recommendation systems is the collaborative filtering method that predicts users’ ratings based on the rating information from users with similar preferences. In order to predict the preferences of users, we need adequate information about users’ interactive information on items. Otherwise, it is very difficult to make accurate predictions for users without adequate information. This phenomenon is called the cold-start problem. In this paper, we propose a multi-domain recommendation system that utilizes the rating information of multiple domains. We propose a method that calculates the weight of each auxiliary domain to maximize the confidence of predicted ratings from multiple auxiliary domains and verify the performance of the proposed method through extensive experiments. As a result, we demonstrate that our algorithm produces better recommendation results compared to the classical algorithms simply utilized in multiple domain settings.
Data Modelling Method for Real-Time Advertising Service Based on Viewer Reaction and Intention in Online Broadcasting
Seongju Kang, Chaeeun Jeong, Kwangsue Chung
http://doi.org/10.5626/JOK.2020.47.11.1086
The interaction between the existing advertising service and the user is limited. To provide a personalized advertising service, advertisement systems should predict the user"s preference based on the user"s profile and the user-content relationship. Many recommendation schemes have been studied to predict the preferences of users. However, the existing recommendation system is difficult to guarantee real-time preference prediction as it performs a calculation of the matrix with high computational complexity. In this paper, we propose a data modeling method for real-time advertising services based on the reaction and intention of viewers. To predict the user"s preference in real-time, the user"s historical data is modeled in a tree structure. The tree structure allows us to retrieve and compare the data with logarithmic time complexity. To improve the accuracy of the recommendation, we have proposed a recommendation algorithm that considers both the user"s positive and negative evaluations. Finally, we have evaluated the performance of the proposed method through various methods.
Reducing the Learning Time of Code Change Recommendation System Using Recurrent Neural Network
Byeong-il Bae, Sungwon Kang, Seonah Lee
http://doi.org/10.5626/JOK.2020.47.10.948
Since code change recommendation systems select and recommend files that needing modifications, they help developers save time spent on software system evolution. However, these recommendation systems generally spend a significant amount of time in learning accumulated data and relearning whenever new data are accumulated. This study proposes a method to reduce the time spent on learning when using Code change Recommendation System using Recurrent Neural Network (RNN-CRS), which works by avoiding the learning that is unlikely to contribute to new knowledge. For the five products used in the experimental evaluation, our proposed method reduced the time to relearn data and re-generate a learning model by as much as 49.08%-68.15%, and by 10.66% in the least effective case, compared to the existing method.
Route Recommendation based on Dynamic User Preference on Road Networks
http://doi.org/10.5626/JOK.2019.46.1.77
The current location based services provide maps and nearby information, or provide a route to a specific destination. A route recommendation system recommends the best route that suits the evaluation criteria for each user. The existing personalized path recommendation system recommends the route under the assumption that the user’s preference is constant regardless of the change of the time zone. However, there is a problem in that it does not reflect requirements that important factors to users can be different for each time zone, such as importance of moving distance in morning time and importance of risk in late time. In this paper, we propose a Dijkstra algorithm considering time attributes to overcome this limitation. In addition, we suggest an efficient algorithm that can search the path reflecting the change of the weight of the preference factor according to the time zone using the G-tree index structure that effectively expresses the road network.
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