國際全球資訊網大會（The Web Conference，簡稱WWW會議）是由國際全球資訊網會議委員會發起主辦的國際頂級學術會議，創辦於1994年，每年舉辦一屆，是CCF-A類會議。WWW 2020將於2020年4月20日至4月24日在台灣台北舉行。本屆會議共收到了1129篇長文投稿，錄用217篇長文，錄用率為19.2%。
哈爾濱工業大學社會計算與資訊檢索研究中心有1篇長文被WWW 2020錄用，下面是論文簡要資訊及摘要：
論文名稱：Keywords Generation Improves E-Commerce Session-based Recommendation
作者：劉元興，任昭春，張偉男，車萬翔，劉挺，殷大偉
單位：哈爾濱工業大學，山東大學，京東
摘要：透過探索細粒度的使用者行為，基於會話的推薦利用使用者在短期內的行為預測使用者的下一個動作。前人的工作僅僅利用了最後一次點選動作作為監督訊號。在電商場景中，由於低包容性問題（即許多滿足使用者購物意圖的相關產品被推薦系統所忽略），具有難以捉摸的點選行為和大規模的商品使這個任務具有挑戰性。由於具有不同ID的相似產品可能具有相同的意圖，因此我們認為，會話中的文字資訊（例如，商品標題的關鍵字）可以用作額外的監督訊號，以透過學習相似產品中更多的共同意圖來解決上述問題。因此，為了提高基於電商會話的推薦的效能，我們根據當前會話中的點選順序生成關鍵字來推斷使用者的意圖。
在本文中，我們提出了帶有關鍵字生成的基於電商會話的推薦模型（ESRM-KG）。具體地，ESRM-KG模型首先將輸入的點選序列編碼為高維向量表示；然後利用一種雙線性解碼，預測當前會話中的下一個動作；同時ESRM-KG模型處理其編碼器的高維表示，以為整個會話生成可解釋的關鍵字。我們在大規模的電商資料集上進行了大量的實驗。我們的實驗結果表明，藉助關鍵字生成，ESRM-KG模型的效能優於最新的基線。我們還透過樣例分析來討論關鍵字生成如何幫助基於電商會話的推薦。
Abstract: By exploring fine-grained user behaviors, session-based recommendation predicts a user’s next action from short-term behavior sessions. Most of the previous work learns about a user’s implicit behavior by merely taking the last click action as the supervision signal. However, in e-commerce scenarios, large-scale products with elusive click behaviors make such task challenging because of the low inclusiveness problem, i.e., many relevant products that satisfy the user’s shopping intention are neglected by recommenders. Since similar products with different IDs may share the same intention, we argue that the textual information (e.g., keywords of product titles) from sessions can be used as additional supervision signals to tackle the above problem through learning more shared intention within similar products. Therefore, to improve the performance of e-commerce session-based recommendation, we explicitly infer the user’s intention by generating keywords entirely from the click sequence in the current session.
In this paper, we propose the e-commerce session-based recommendation model with keywords generation (abbreviated as ESRM-KG) to integrate keywords generation into e-commerce session-based recommendation. Specifically, the ESRM-KG model firstly encodes an input action sequence into a high dimensional representation; then it presents a bi-linear decoding scheme to predict the next action in the current session; synchronously, the ESRM-KG model addresses incepts the high dimensional representation of its encoder to generate explainable keywords for the whole session. We carried out extensive experiments in the context of click prediction on a large-scale real-world e-commerce dataset. Our experimental results show that the ESRM-KGmodel outperforms state-of-the-art baselines with the help of keywords generation. We also discuss how keywords generation helps the e-commerce session-based recommendation with case studies and error analysis.