Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022)

Abstract: A key challenge of Conversational Recommendation Systems (CRS) is to integrate the recommendation function and the dialog generation function smoothly. Previous works employ graph neural networks with external knowledge graphs (KG) to model individual recommendation items and integrate KGs with language models through attention mechanism for response generation. Although previous approaches prove effective, there is still room for improvement. For example, KG-based approaches only rely on entity relations and bag-of-words to recommend items and neglect the information in the conversational context. We propose to improve the usage of dialog context for both recommendation and response generation using an encoding architecture along with the self-attention mechanism of transformers. In this paper, we propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder to integrate the recommendation and the dialog generation better. The proposed item encoder learns to map item metadata to embeddings reflecting the rich information of the item, which can be matched with dialog context. The PLM then consumes the context-aware item embeddings and dialog context to generate high-quality recommendations and responses. Experimental results on the benchmark dataset ReDial show that our model obtains state-of-the-art results on both recommendation and response generation tasks.

## introduction an automated conversational recommendation system (crs) @xcite is intended to interact with users and provide accurate product recommendations (e.g., movies, songs, and consumables). it has been a focal point of research lately due to its potential applications in the 1 code is available online https://github.com/ by2299/mese e-commerce industry. traditional recommendation systems collect user preferences from implicit feedback such as click-through-rate @xcite or purchase history and apply collaborative filtering @xcite or deep learning models @xcite to construct latent spaces for user preferences. unlike traditional recommendation systems, crss directly extract user preferences from live dialog history to precisely address the users' needs. although some progress has been made in this area, there is still room for improvement. first, previous crss @xcite @xcite track entities mentioned in the dialog context, and then search related items in knowledge graphs to recommend to users. however, these systems require a named-entity recognition (ner) module to extract mentioned entities from the dialog context. thus we need to collect additional domain-specific data to train the ner module. in practice, such ner modules have deficient performance, leading to a bad accuracy of crs. second, existing crss built upon graph neural networks @xcite cannot quickly scale up or respond to rapid changes of the underlining entities. in e-commerce, items for recommendation change frequently due to constant updates of merchants and products. existing approaches require either re-training the entire system when the structure of knowledge graph changes @xcite or adding complex architectures on top to be adaptive @xcite . a more flexible architecture can help the system react to rapid changes and adapt itself to new items. moreover, meta-information about the items can be leveraged. similar information can be found in both dialog context and item meta-information. for example, in a movie recommendation setting, words like "crime, gangsters, etc." are likely to exist in the dialog context when a user is searching for crime movies. in the synopsis of a crime movie, such keywords are likely to exist as well. ideally, an alignment between the semantics of dialog context and the item meta-information can be used to improve system's performance. driven by the motivations above, we present a metadata enhanced learning approach via semantic extraction from dialog context i.e. mese. the major components of mese contain a pre-trained language model (plm) and an item encoder architecture. the item encoder takes item metadata as input and outputs a vector embedding. by jointly training the encoder and the plm, the entire system can extract co-occurring information between dialog context and item metadata, and item encoders can systematically construct representations reflecting this alignment. item embeddings are then consumed with dialog context by the selfattention mechanism of the plm. this mechanism smoothly integrates dialog context and item information well into the recommendation and response generation tasks. the key contributions of this paper are summarized as follows: this paper presents mese, a novel crs framework that considers both item metadata and dialog context for recommendations. our model employs a simple yet effective item metadata encoder that learns to represent rich item information during training. such encoder can adapt to database changes quickly and is independent of task-specific architectures. extensive experiments on standard dataset redial demonstrate that mese outperforms previous state-of-the-art methods on both response generation and recommendation with a large margin. ## related work the current crs paradigm contains two major modules: a recommendation module that suggests items based on conversational context and a response generation module that generate responses based on dialog history and the recommended items. integrating these two modules to perform well on both tasks has been a major challenge. @xcite leverage external knowledge and employees graph neural networks as the backbone to model entities and entity relations in the knowledge graph (kg) to enhance performance. @xcite introduce a word-level kg @xcite to the system with semantic fusion @xcite to enhance the semantic repre-sentations of words and items. since item information and dialog context are processed separately in the above approaches, they loss integrated sentencelevel information. we propose to condition recommendation on integrated contextual information of both dialog context and mentioned entity information. more recent works adopt pre-trained language models (plm) @xcite @xcite and template-based methods to facilitate response generation. @xcite generate a response template containing a mixture of contextual words and slot locations to incorporate recommended items better. @xcite expand the vocabulary list of the plm to include items to unify the process of item recommendation with response generation. we propose to enhance our plm with an item metadata encoder to extract context-aware representations by jointly training on both recommendation and response generation tasks. we also generate response templates with slot locations to better incorporate recommended items into responses. our work is also inspired by studies from other areas. recent works have shown that crossmodality training across vision and language tasks can lead to outstanding results in building multimodal representations @xcite . in @xcite , a largescale transformer-based model is adapted with cross-modal encoders to connect visual and linguistic semantics and pre-trained on vision-language pairs to learn cross-modality relationships. prompt tuning @xcite methods show that plms are capable of integrating different sources of information into the same embedding space. in terms of using plm as a recommendation system, @xcite train a bidirectional self-attention model to predict masked items and achieve remarkable results. inspired by the above studies, we propose to use an encoder module to map item meta-information to an embedding space. by jointly training on dialog context and encoded item representations, the system can align these two information streams by fusing the semantic spaces. ## approach in this section, we present our framework mese that integrates item metadata with dialog context. we first introduce how to encode item metadata and how to blend item information into dialog con-text. we then illustrate how the recommendation module and the response generation module are built. finally, we describe the training objectives and the testing process. ## experiments in this section, we discuss the datasets used, experimental setup, experimental results on both recommendation and language metrics, and report analysis results with ablation studies. ## conclusion and future work in this paper, we introduced mese, a novel crs framework. by utilizing item encoders to construct embeddings from metadata, mese can provide high-quality recommendations that align with the dialog history. our approach yields better performance than existing state-of-the-art models. ablation studies explain the reason behind this performance gain. as for future work, we will consider applying this approach to a broader domain of crs datasets. currently, we only experiment on movie recommendations. however, the encoder of mese is flexible and can potentially work well with crossmodality tasks. multi-modal crss can be explored in the e-commerce domain with mese.