Session-based recommendation (SR) has become an important and popular component of various e-commerce platforms, which aims to predict the next interacted item based on a given session. Most of existing SR models only focus on exploiting the consecutive items in a session interacted by a certain user, to capture the transition pattern among the items. Although some of them have been proven effective, the following two insights are often neglected. First, a user's micro-behaviors, such as the manner in which the user locates an item, the activities that the user commits on an item (e.g., reading comments, adding to cart), offer fine-grained and deep understanding of the user's preference. Second, the item attributes, also known as item knowledge, provide side information to model the transition pattern among interacted items and alleviate the data sparsity problem. These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation. Specifically, a given session is modeled on microbehavior level in MKM-SR, i.e., with a sequence of item-operation pairs rather than a sequence of items, to capture the transition pattern in the session sufficiently. Furthermore, we propose a multitask learning paradigm to involve learning knowledge embeddings which plays a role as an auxiliary task to promote the major task of SR. It enables our model to obtain better session representations, resulting in more precise SR recommendation results. The extensive evaluations on two benchmark datasets demonstrate MKM-SRâĂŹs superiority over the state-of-the-art SR models, justifying the strategy of incorporating knowledge learning.
Purpose Analyzing the sentiment orientation of each product aspect/feature might be sufficient to assist the customer to make purchase/usage decisions, but such level of information obtained by sentiment analysis is not detailed enough to assist the company in making product improvement or design decisions. Therefore, this paper aims to propose a novel method to extract more detailed information of the product. Design/methodology/approach This paper proposed to use a set of trivial lexical-Part-of-Speech patterns to prepare candidate corpus and then adopted a topic model to find the optimal number of topics and get the words distributions in each topic. Finally, combined a priori analysis and compactness rules, the authors found out the expected strong rules in each topic, which make up the final problems. Findings Experimental results on a real-life data set from Xiaomi forum showed the proposed method can extract the product problems effectively. The authors also explained the errors of experiment, which suggested the direction for future research. Originality/value This paper proposed a novel method to obtain information of product problems in detail, which will be useful to assist companies to improve their product performance.
Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many realworld applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the model continuously. Some previous works have proved that storing typical samples of old relations in memory can help the model keep a stable understanding of old relations and avoid forgetting them. However, most methods heavily depend on the memory size in that they simply replay these memorized samples in subsequent tasks. To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. Specifically, the prototype embedding for a specific relation is computed based on memorized samples of this relation, which is collected by K-means algorithm. The prototypes of all observed relations at current learning stage are used to re-initialize a memory network to refine subsequent sample embeddings, which ensures the model's stable understanding on all observed relations when learning a new task. Compared with previous CRE models, our model utilizes the memory information sufficiently and efficiently, resulting in enhanced CRE performance. Our experiments show that the proposed model outperforms the state-of-the-art CRE models and has great advantage in avoiding catastrophic forgetting. The code and datasets have been released on https://github.com/fd2014cl/RP-CRE.
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