消費者在交易過程中若購買多件商品，將形成商品購物籃，該購物籃揭示消費者的交易歷史資訊與偏好，使商家能利用購物籃內消費資訊，分析實際的行銷成效。隨著商品的多樣性與消費頻率的快速增長，利用頻繁模式挖掘等關聯式分析方法已不敷使用，商家需要更為精確，並能反映複雜消費模式的工具以擬訂銷售策略。在此脈絡之下便衍生出購物籃內推薦系統，該系統根據消費者過往的交易紀錄以推薦相關商品，減少消費者的購物檢索成本，商家也能依照推薦結果優化銷售策略，進而提升獲利。而在購買過程中，商品價格為消費者是否購買的重要依據與衡量商品價值的媒介。若進一步探討交易歷史資訊、價格與商品之間的關聯性，並預測出消費者的購買品項，將有助於商家分析市場的動向與變化，提出更精準的營運策略。過往針對購物籃內推薦系統的研究中，在處理多樣性和複雜關係方面仍具有其侷限性，且多數文獻缺乏探討價格等外部因素，並從商家角度為切入點以提出具體的行銷策略。本研究以類神經網路模型為基礎，輸入消費者編號、商品編號、商品價格與購買數量等歷史交易資訊，藉由深度因子分解機適用於稀疏性資料的特性，找出購物籃內商品之間的關聯，建構預測消費者購買品項之購物籃內推薦系統。本研究使用公開資料集Dunnhumby和Ta Feng以驗證模型的預測準確度和穩健性，相較於其他三種類神經網路之績效與運算時間成本，深度因子分解機整體表現優異。且根據t-SNE技術、SI指標和價格敏感度分析，發現加入價格後消費者呈現出明顯的分群，遂模型考慮了價格因素後，得以獲取購物籃中商品之間的相關性，並依照商品互補與替代關係，可提供商家擬定銷售策略的方向，藉以提升其管理效率和獲利能力。

The consumers of online shopping platforms typically purchase multiple items at a time in their shopping basket. This allows merchants to use the information within the shopping basket to analyze their marketing effectiveness. With the rapid growth in product diversity and consumption frequency, traditional association analysis methods such as frequent pattern mining and other statistical methods are no longer efficient enough. Moreover, the price factor has not been considered in prior work and there is a scarcity of literature that approaches the subject from a management perspective. In this study, we propose a within-basket recommendation system, which recommends related products based on consumers' historical transaction data. Merchants can optimize sales strategies according to recommendation results, thus increasing profitability. In addition, we apply deep factorization machines (DeepFM) to build the model and use publicly available datasets Dunnhumby and Ta Feng to verify the model's prediction accuracy and robustness. Compared to the other three types of neural network models, DeepFM demonstrates superior performance in terms of prediction accuracy. Additionally, the model performance is significantly improved after considering the price factor. We also used the t-distributed Stochastic Neighbor Embedding (t-SNE) technique and Silhouette Index (SI) as clustering evaluation metrics. The results show that the model performs better at clustering after incorporating the price factor. Finally, through price sensitivity analysis, the model captures the correlations among products in the shopping basket. This enables merchants to determine optimal pricing ranges and develop specific sales strategies, thereby enhancing management efficiency and profitability.

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
Barkan, O., & Koenigstein, N. (2016, September). Item2vec: neural item embedding for collaborative filtering. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
Bhutto, M. H., Tariq, B., Azhar, S., Ahmed, K., Khuwaja, F. M., & Han, H. (2022). Predicting consumer purchase intention toward hybrid vehicles: testing the moderating role of price sensitivity. European Business Review, 34(1), 62-84.
Briesch, R. A., Krishnamurthi, L., Mazumdar, T., & Raj, S. P. (1997). A comparative analysis of reference price models. Journal of Consumer Research, 24(2), 202-214.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12, 331-370.
Chen, S., Moore, J. L., Turnbull, D., & Joachims, T. (2012, August). Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 714-722).
Chiranjivdas09. (n.d.). Ta Feng Grocery Dataset. kaggle. Retrieved from https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset (January 25, 2024)
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
Danaher, P. J., Smith, M. S., Ranasinghe, K., & Danaher, T. S. (2015). Where, when, and how long: Factors that influence the redemption of mobile phone coupons. Journal of Marketing Research, 52(5), 710-725.
Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics. Information Systems Research, 13(3), 316-333.
Dodds, W. B., & Monroe, K. B. (1985). The effect of brand and price information on subjective product evaluations. ACR North American Advances.
Entezari, N., Papalexakis, E. E., Wang, H., Rao, S., & Prasad, S. K. (2021, December). Tensor-based complementary product recommendation. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 409-415). IEEE.
Faggioli, G., Polato, M., & Aiolli, F. (2020, July). Recency aware collaborative filtering for next basket recommendation. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 80-87).
Firat gonen. (n.d.). Dunnhumby - The Complete Journey. kaggle. Retrieved from https://www.kaggle.com/datasets/frtgnn/dunnhumby-the-complete-journey (January 25, 2024)
Fowers, J., Ovtcharov, K., Papamichael, M., Massengill, T., Liu, M., Lo, D., ... & Burger, D. (2018, June). A configurable cloud-scale DNN processor for real-time AI. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA) (pp. 1-14). IEEE.
Gabel, S., & Timoshenko, A. (2022). Product choice with large assortments: A scalable deep-learning model. Management Science, 68(3), 1808-1827.
Gallego, G., & Wang, R. (2014). Multiproduct price optimization and competition under the nested logit model with product-differentiated price sensitivities. Operations Research, 62(2), 450-461.
Garg, D., Gupta, P., Malhotra, P., Vig, L., & Shroff, G. (2019, July). Sequence and time aware neighborhood for session-based recommendations: Stan. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1069-1072).
Gong, S. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software, 5(7), 745-752.
Gopalan, P., Hofman, J. M., & Blei, D. M. (2015, July). Scalable Recommendation with Hierarchical Poisson Factorization. In UAI (pp. 326-335).
Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., & Sharp, D. (2015, August). E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1809-1818).
Guidotti, R., Rossetti, G., Pappalardo, L., Giannotti, F., & Pedreschi, D. (2018). Personalized market basket prediction with temporal annotated recurring sequences. IEEE Transactions on Knowledge and Data Engineering, 31(11), 2151-2163.
Han, S., Gupta, S., & Lehmann, D. R. (2001). Consumer price sensitivity and price thresholds. Journal of Retailing, 77(4), 435-456.
Haykin, S. (1996). Neural networks expand SP's horizons. IEEE Signal Processing Magazine, 13(2), 24-49.
Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Hsu, C.-L., Chang, C.-Y., & Yansritakul, C. (2017). Exploring purchase intention of green skincare products using the theory of planned behavior: Testing the moderating effects of country of origin and price sensitivity. Journal of Retailing and Consumer Services, 34, 145-152.
Hu, H., He, X., Gao, J., & Zhang, Z. L. (2020, July). Modeling personalized item frequency information for next-basket recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1071-1080).
Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
Kalwani, M. U., Yim, C. K., Rinne, H. J., & Sugita, Y. (1990). A price expectations model of customer brand choice. Journal of Marketing Research, 27(3), 251-262.
Kalyanaram, G., & Winer, R. S. (1995). Empirical generalizations from reference price research. Marketing Science, 14(3_supplement), G161-G169.
Khodabandehlou, S., & Zivari Rahman, M. (2017). Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. Journal of Systems and Information Technology, 19(1/2), 65-93.
Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics, 11(1), 141.
Kraus, M., & Feuerriegel, S. (2019, July). Personalized purchase prediction of market baskets with wasserstein-based sequence matching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2643-2652).
Le, D. T., Lauw, H. W., & Fang, Y. (2017). Basket-sensitive personalized item recommendation. IJCAI.
Lee, K. H., Bonn, M. A., & Cho, M. (2015). Consumer motives for purchasing organic coffee: The moderating effects of ethical concern and price sensitivity. International Journal of Contemporary Hospitality Management, 27(6), 1157-1180.
Li, M., Ariannezhad, M., Yates, A., & de Rijke, M. (2023). Who will purchase this item next? Reverse next period recommendation in grocery shopping. Recommender Systems, 1(2), 1.
Li, M., Bao, X., Chang, L., & Gu, T. (2022). Modeling personalized representation for within-basket recommendation based on deep learning. Expert Systems with Applications, 192, 116383.
Li, X., & She, J. (2017, August). Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 305-314).
Liu, J., Chen, W., Yang, J., Xiong, H., & Chen, C. (2022). Iterative prediction-and-optimization for E-logistics distribution network design. INFORMS Journal on Computing, 34(2), 769-789.
Liu, T., Shen, H., Chang, L., Li, L., & Li, J. (2023). Iterative heterogeneous graph learning for knowledge graph-based recommendation. Scientific Reports, 13(1), 6987.
Liu, Z., Li, X., Fan, Z., Guo, S., Achan, K., & Philip, S. Y. (2020, December). Basket recommendation with multi-intent translation graph neural network. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 728-737). IEEE.
Liu, Z., Wan, M., Guo, S., Achan, K., & Yu, P. S. (2020). Basconv: Aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network. In Proceedings of the 2020 SIAM International Conference on Data Mining (pp. 64-72). Society for Industrial and Applied Mathematics.
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender Systems Handbook, 73-105.
Maheswari, K., & Priya, P. P. A. (2017, March). Predicting customer behavior in online shopping using SVM classifier. In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (pp. 1-5). IEEE.
Mantha, A., Arora, Y., Gupta, S., Kanumala, P., Liu, Z., Guo, S., & Achan, K. (2020, May). A large-scale deep architecture for personalized grocery basket recommendations. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3807-3811). IEEE.
Matevž, K., & Tomaž, P. (2017). Diversity in recommender systems – A survey. Knowledge-based Systems, 123, 154-162.
Meng, Z., McCreadie, R., Macdonald, C., & Ounis, I. (2021). Variational Bayesian representation learning for grocery recommendation. Information Retrieval Journal, 24(4-5), 347-369.
Pan, C., & Li, W. (2010, June). Research paper recommendation with topic analysis. In 2010 International Conference on Computer Design and Applications (Vol. 4, pp. V4-264). IEEE.
Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
Peng, B., Ren, Z., Parthasarathy, S., & Ning, X. (2021). HAM : Hybrid Associations Models for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4838-4853.
Ramirez, E., & Goldsmith, R. E. (2009). Some antecedents of price sensitivity. Journal of Marketing Theory and Practice, 17(3), 199-214.
Rao, A. R., & Monroe, K. B. (1989). The effect of price, brand name, and store name on buyers??perceptions of product quality: An integrative review. Journal of Marketing Research, 26(3), 351-357.
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010, April). Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (pp. 811-820).
Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L. (2011, July). Fast context-aware recommendations with factorization machines. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 635-644).
Reutterer, T., Platzer, M., & Schroder, N. (2021). Leveraging purchase regularity for predicting customer behavior the easy way. International Journal of Research in Marketing, 38(1), 194-215.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
Rudolph, M., Ruiz, F., Mandt, S., & Blei, D. (2016). Exponential family embeddings. Advances in Neural Information Processing Systems, 29.
Ruiz, F. J., Athey, S., & Blei, D. M. (2020). SHOPPER. The Annals of Applied Statistics, 14(1), 1-27.
Su, X. (2007). Intertemporal pricing with strategic customer behavior. Management Science, 53(5), 726-741.
Togashi, R., Otani, M., & Satoh, S. I. (2021, March). Alleviating cold-start problems in recommendation through pseudo-labelling over knowledge graph. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 931-939).
Truong, M., Gupta, A., Ketter, W., & van Heck, E. (2022). Buyers' strategic behavior in B2B multichannel auction markets: When an online posted price channel is incorporated into a Dutch auction system. Information Systems Research, 33(4), 1344-1367.
Van Maasakkers, L., Fok, D., & Donkers, B. (2023). Next-basket prediction in a high-dimensional setting using gated recurrent units. Expert Systems with Applications, 212, 118795.
Wakefield, K. L., & Inman, J. J. (2003). Situational price sensitivity: the role of consumption occasion, social context and income. Journal of Retailing, 79(4), 199-212.
Wan, M., Wang, D., Liu, J., Bennett, P., & McAuley, J. (2018, October). Representing and recommending shopping baskets with complementarity, compatibility and loyalty. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1133-1142).
Xu, E., Yu, Z., Sun, Z., Guo, B., & Yao, L. (2023). Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity. ACM Transactions on Intelligent Systems and Technology, 14(3), 1-19.
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., ... & Gai, K. (2018, July). Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1059-1068).