CR - GIS : Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022)

Abstract: Conversational recommendation systems (CRS) aim to determine a goal item by sequentially tracking users’ interests through multi-turn conversation. In CRS, implicit patterns of user interest sequence guide the smooth transition of dialog utterances to the goal item. However, with the convenient explicit knowledge of knowledge graph (KG), existing KG-based CRS methods over-rely on the explicit separate KG links to model the user interests but ignore the rich goal-aware implicit interest sequence patterns in a dialog. In addition, interest sequence is also not fully used to generate smooth transited utterances. We propose CR-GIS with a parallel star framework. First, an interest-level star graph is designed to model the goal-aware implicit user interest sequence. Second, a hierarchical Star Transformer is designed to guide the multi-turn utterances generation with the interest-level star graph. Extensive experiments verify the effectiveness of CR-GIS in achieving more accurate recommended items with more fluent and coherent dialog utterances.

## introduction traditional recommendation systems often interact with users in a one-shot, one-directional manner @xcite , that is, users passively receive the static recommendation list and the recommendation system lacks the ability to understand and proactively guide the dynamic shift of users' interests. conversational recommendation systems (crs) @xcite solve these problems by supporting multi-turn goaloriented @xcite dialog to proactively track and guide real-time user interests shift @xcite . in current studies, one most popular way of crs is to determine an item meeting the user preference through multi-turn conversation. regarding the item to be determined as the goal, we regard this kind of crs as "goal-aware" crs. in this paper, we suppose that the performance of goal-aware crs is highly dependent on a goal-aware sequence of user interests which is expressed in the form of logically transited utterance sequence in dialog. from the view of recommendation, in a goal-aware crs dialog, a proper dynamic user preference at any time should be coherent to the preorder interests and towards the final goal in the interest sequence. from the view of conversation, a goal-aware crs dialog is a smoothly transited sequence of utterances guided by the sequence of user interest. therefore, modeling the goal-aware sequence of user interests is essential for goal-aware crs. for example, in the "dialog" of figure 1 , there are similarities in storyline or other aspects between neighbor entities in the interest sequence, i.e., "shutter island" → "inception" → "leonardo" → "source code", and between each interest entity and the recommendation goal "the butterfly effect". for modeling the goal-aware interest sequence, kg-based crs methods are widely studied using knowledge graph (kg) @xcite @xcite to track the interest sequence in the dialog @xcite . although kg's explicit logical links between interest entities greatly facilitate the modeling of interest sequence, it also leads to over-rely on kg knowledge and weakens the key role of dialogue behavior in crs. specifically, we note two major consequent issues: (1) from the view of recommendation, most kg-based crs model the user preference only on the explicit separate interest links in kg and ignore the rich implicit interests sequence in dialogue which is absent in kg knowledge. for example, in the "dialog" of figure 1 , each interest entity may have implicit semantic relations with the goal "the butterfly effect" beyond kg links. furthermore, the goal of the sequence is also absent in the user preference modeling. (2) from the view of conversation, current kg-based crs mainly use entities in the interest sequence to enhance the semantic generation of every single response instead of using the entire sequence to enhance the smooth transition of multiple utterances towards the recommendation goal. to address these two issues, we propose cr-gis, which jointly improves the conversation and recommendation by modeling goal-aware interest sequence in crs. to this end, we design a novel parallel star structure with two advantages: (1) for the first issue of implicit relations modeling in goal-aware interests sequence, on the base of preencoded explicit knowledge from kg, we propose an interest-level star graph to encode the implicit relations between interest entities in dialog. the satellite nodes of the star graph are interest entities in the current ongoing dialog, which are sequenced by their adjacency relationship in the dialog. the key advantage of interest-level star graph is that the central star node, which connects all satellite nodes, can be fused to the recommendation goal with the mutual information maximization (mim) method. this alignment makes the interest sequence modeling goal-oriented. (2) for the second issue of smooth transition in goal-aware conversation, to be paralleled to the interest-level star graph, we design a hierarchical star transformer encoder @xcite for words and utterances. by aligning the utterance state representation of the "utterancelevel star" with the "interest-level star", we enhance the crs to generate smoothly transited utterances towards the recommendation goal. our contributions are summarized as follows: ## related work current crs can be divided into two types. one is the recommendation-based crs which aims to ask users questions about interests over pre-define slots @xcite @xcite @xcite and make responses considering users' feedback @xcite . recommendation-based crs mainly suffers from the inflexibility of pre-defined templates. the other is the generation-based crs @xcite which understands user preferences @xcite @xcite and generates human-like responses @xcite in line with user interests. closely related to our work, @xcite and @xcite integrate kg knowledge to understand users' interest. however, they simply aggregate entities of kg in the utterance, instead of exploiting the implicit interest sequence in the conversation. @xcite and @xcite adopt reasoning-based methods to predict the shift direction of user interest, but also limits to the explicit interest sequence in kg. suppose we have a crs corpus d and a kg g parallel to d, in which the interest entities mentioned in d are linked to the entities in g. @xmath0, u 2 , . . . , u n } is the conversation history, where u @xmath1,1 , w i,2 , . . . , w i,m } is the word token sequence in the i-th utterance. @xmath2, s 2 , . . . , s k } is the interest entity in each utterance of u , and s @xmath3, i.e., s @xmath4. in a response @xmath5, y 2 , . . . , y m }, a recommendation goal entity set @xmath6, g 2 , . . . , g t } is identified in advance, where g @xmath7, i.e., g @xmath8. n,m,k,t represent the length of the historical utterance sequence, the length of the token sequence, the length of the interest entity sequence and the number of recommendation goals, respectively. our task is to learn a recommendation model and a response generation model p (y |u, s, g) with the d and g. the former model the implicit user interests sequence through s and g and help the latter to generate responses that smoothly progress to the recommendation goal g. ## problem formalization shutter island is one of my favorite murder mystery. …. me too. inception starring leonardo has the same charm. yes, i agree. the parallel world in the source code also amazes me. could you recommend me some others? i recommend the butterfly effect, which also has an unpredictable time cycle. …. … … kg dialog 𝑢 1 𝑢 2 𝑢 3 𝑌 rgcn lookup table 𝒔 1 𝒔 2 𝒔 𝑘 𝒔 3 𝒔 … 𝒔 4 𝒔 𝑥 interest-level star graph construction sgnn-l 𝒔 1 𝐿 𝒔 2 𝐿 𝒔 𝑘 𝐿 𝒔 3 𝐿 𝒔 … 𝐿 𝒔 𝑥 𝐿 sequence encoder 𝑷 𝒓𝒆𝒄 sgnn*l layers interest-level star graph mim 𝒘 1,1 𝑐𝑙𝑠 𝒘1,2 𝒘1,𝑚 𝒘1,3 𝒔𝑢,1 𝒘1,… 𝒘1,4 𝒘 𝑛,1 𝑐𝑙𝑠 𝒘𝑛,2 𝒘𝑛,𝑚 𝒘𝑛,3 𝒔𝑢,𝑛 𝒘𝑛,… 𝒘𝑛,4 token-level star graph construction token-level star transformer 𝒖 1 𝒖 2 𝒖 𝑛 𝒖 3 𝒖 … 𝒖 4 𝒔 𝑢 … utterance-level star transformer vanilla transformer decoder utterance-level star graph construction goal-oriented recommendation goal-aware conversation align star graphs user embedding utterance representations hierarchical star transformer encoder 𝒖 1 𝒖 … 𝒖 𝑛 𝒈 𝑡 𝒈 … 𝒈 1 response 𝒔 𝑥 𝐿 𝑠 1 𝑠 2 𝑠 3 𝑠 4 𝑔 1 𝑢 1 𝑢 2 𝑢 3 … 𝑢 𝑛 𝑔 1 𝑔 𝑡 𝑠 1 𝑠 2 𝑠 3 𝑠 𝑘 … … figure 1: the architecture of the proposed cr-gis model. ## conclusion in this paper, we propose cr-gis to model goalaware interest sequence in crs with an interestlevel star graph whose star node is oriented to the recommendation goal. by using goal-oriented interest-level star graph to guide a utterance-level star graph, cr-gis significantly improve the goalaware ability in generating smoothly transited response. extensive experiments verify the superiority of cr-gis in terms of recommendation, conversation, and reaching recommendation goals. opendialkg context i like j.k.rowling. me, too! she's great! did you want to know some cool stuff about her? yes, i want to know. transformer yes, i did. it was released in 2013. it was written by the film. (incoherence) kbrd jk rowlings wrote this book (less-informativeness) kgsf yes, i can recommend harry potter and the sorcerer's apprentice. (less-coherence, less-informativeness) revcore yes, i think she was in the harry potter and the philosopher's stone. did you know that one also starred in the philosopher's stone? (fact inconsistency, i.e., not "was in", fact is "wrote") crfr yes, he wrote the tales of beedle the bard and the cuckoo 's calling. have you seen any (less-fluency, gender inconsistency) c 2 -crs yes, i didn't know that. did he also write the harry potter series? (less-informativeness, gender inconsistency) cr-gis sure, she wrote the tales of beedle the bard, the silkworm, which is a series. have you read any of those? ground-truth did you know she wrote two books called the silkworm as well as the book the cuckoo's calling? tg-redial context 好啊，我比较喜欢人性题材的，真实改编的电影。 well, i prefer movies based on human nature and reality. 推荐一部《现代启示录》，真的展现出人性混沌无善恶，残酷壮丽一瞬间。 recommend a apocalypse now, it really shows the chaos of human nature, no good and evil, cruel and magnificent moment. 你说这个影片能展现出人性混沌无善恶，残酷壮丽一瞬间，这个评价很高啊，一会看看。 you said that this movie can show the chaos of human nature without good and evil, cruel and magnificent for a moment, the evaluation is very high, i will see it later. 推荐你看《势不两立》，是关于人性的背叛，很现实的电影，两个主角都很饱满，对人性的塑造 非常到位。 i recommend you to watch the edge, which is a very realistic film about the betrayal of human nature. the two protagonists are full and the shaping of human nature is very in place. 感觉这样的电影展现的就是：这样的人越是苦难，越是闪耀人性光辉。能让我们感悟很多吧？ i feel that what such a movie shows is: the more suffering such a person is, the more shining the brilliance of human nature. can we learn a lot? transformer 还有一部关于人性的电影，叫《现代启示录》，这也是一部关于 there is also a movie about human nature called apocalypse now, which is also a movie about (retell, nonfluency) kbrd 看过《unk》吗，这是一部好电影，人性的原版看起来要看起来，但很爽。 have you @xmath9, it's a good movie, the original version of human nature looks to look good, but it's cool. (less-informativeness) kgsf 《双重人格》非常适合你，影片中的人性，人性，两个极端极端探讨。 the double is very suitable for you, the movie's human nature, human nature, two extreme extreme exploration. (repetition) revcore 《人性污点到底是什么才是烂片之一。还有其他关于人性的电影吗？ what the hell is the human stain is one of the bad movies. are there any other movies about human nature? (nonfluency, incoherence) crfr 《越战创伤》非常好，老帕还挺对我胃口。 casualties of war is very good, and palma is kinda to my taste. (less-coherence, none user-centric) c 2 -crs 行啊，最好是那种反映人性心理社会的讽刺题材电影。 okay, it's better to be a satirical movie that reflects human psychology and society. (less-informativeness) cr-gis 《忏情记》满足你的要求，人性与法律，救赎与出卖。 i confess meets your requirements, human nature and law, redemption and betrayal.