Interrupt me Politely: Recommending Products and Services by Joining Human Conversation (2020)

Abstract: We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. We evaluate performance of RJC is in a number of human-human and human-chat bot dialogues, and demonstrate that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.

## introduction due to the popularity of texting and messaging, in combination with a recent advancement of deep learning technologies, a conversation-based recommendation has become an emerging platform for advertising. while modern conversation platforms offer basic conversation capabilities such as natural language understanding, entity extraction and simple dialogue management, there are still challenges in developing practical applications to support complex use cases such as recommendation, relying on dialogue systems @xcite @xcite . over the last 2 or 3 years, much more precise and powerful recommendation algorithms have been created which are better more effectively assessing users' tastes, and predicting any relevant information that would be of interest to them. most of these approaches rely on machine learning-based collaborative techniques, and do not take into account the huge amount of knowledge, both structured and non-structured, such as prior user utterances in a dialogue, which describe the domain of interest for the recommendation engine @xcite . a conversational advertising agent could have much more commercial potential in comparison with a conventional advertising such as random insertion in a sequence of conversation, as provided by a social advertising network like facebook. but research on this topic is very limited and existing solutions are either based on single round conventional search or a traditional multi round dialog system. web portals such as amazon, ebay, jd, alibaba and others usually only utilize user inputs in the current session, ignoring users' long term preferences, or just perform slot-filling, obtaining the parameters of interest from the user explicitly @xcite . moreover, most of such systems behave very differently from a human when they asked for a recommendation @xcite . humans can quickly establish preferences when asked to make a recommendation for someone they do not know. although rjc is an effective and efficient means of advertising and marketing, nowadays even a conventional advertisement can be significantly improved by simple filters, like preventing ads for poorly-rated products. in this paper, we formulate a broader advertising and recommendation problem learning user preferences implicitly from the previous utterances in an arbitrary problem-solving conversation, not just by asking explicitly about user preferences. we introduce a recommendation by joining a conversation (rjc), a special case of conversational advertisement with a focus on assisting with solving a current customer problem or need being communicated. in rjc scenarios, customers are expected to be fully aware of how and why a product or service being recommended would solve their issues. we consider two types of rjc scenarios: ## sample dialogues with recommendations one of the main requirement for the advertising in the course of cs dialogue is that the relation to the product the user experiences problem with must be obvious, as well as the benefits to the user of relying on this new recommended product to overcome this problem. we start with an example of casual conversation and demonstrate how an advertising utterance can naturally appear. example 1: mike: hey, what's up, dude? peter: not much. i am looking for a dvd to rent but i am fed up with all these. have seen most of them already mike: anything worth seeing at the movie theater? peter: nah. just kids movies, sci-fi and cheesy romantic comedies. rjc-agent: if you are looking for something new you should come to a meeting of the new age alternative films club peter: what is that? rjc-agent: the new age alternative films club gets together every other week and screens the type of films you cannot go at a regular movie theater an utterance of rjc agent can be followed by additional factual questions rjc should be able to answer. example 2: agent: it's a good day today at bank of wealth, my name is heather. how can i help you? customer: i would like to know my remaining money in my account. agent: i'll be glad to help you. may i please get your bank account number and the name on the account? customer: sure, it's tracy q. randall, account number is ****. agent: thank you, let me just check on it. ok, can you, please, verify the last four numbers of your social security id. customer: it is ****. agent: you still have 84 thousand and 65 cents. is there anything else that i could assist you with? customer: yes, if i transfer it to my bank account in lloyds of london, how long will it take? agent: if we do the transaction over the phone or online, our team will still contact you for verification prior sending your money to a different bank. rjc-agent: open account in morgan chase and use zelle quickpay to quickly transfer money to your friends and partners abroad an applicability of the proposed recommendation setting can go beyond cs scenarios. daily conversations are rich in emotion. by expressing emotions, people show their mutual respect, empathy and understanding to each other, and thus improve the relationships @xcite . example 3: riley: are you still auditioning for that skin cream commercial? katie: that just so happens to be the 'in thing'. does not every aspiring actress start off in a commercial? riley: i take it you did not get the part of that 'life and death' sitcom? katie: they did not even let me audition rjc-agent: have you thought about taking acting lessons? have you heard about beverly hills playhouse -acting classes los angeles? in a regular recommendation / advertisement scenario, any popular product or the one meeting the user preferences is considered to be appropriate. conversely, in the rjc scenario a recommended product or service must be related to the product which is the main entity of the problem being resolved. we show the cases of typical customer problems in various domains: 1) a customer does not maintain a positive balance carefully and now wants to avoid nsf in the future. subject of the problem focus of a conversation product to recommend recommended feature search query checking account no overdraft protection saving account linked with checking for overdraft protection x for checking account with overdraft protection hotel @ <loca-@xmath0. examples of seed and recommended products the queries have a placeholder x for product/service name such as account type, accommodation name, air travel company, etc. the role of this placeholder in a query is to assure the respective entity type does occur in an acceptable search result. processing steps in the rjc component are the following: 1) extract noun phrases from utterance. ## how to make recommendation more persuasive? a number of studies including @xcite demonstrated that explanation and persuasion are two important characteristics for convincing users to follow the recommendations. example 4: customer: you charged me unfair nsf but i maintained a positive balance on my account. agent: we have to charge nsf to maintain our income, so you should maintain minimum balance. good rjc-agent: i recommend you a product such that you avoid a negative balance. you should get our product linked checkingsaving account with overdraft protection, so that nsf never happens again. marginally relevant but unpersuasive-agent: open new account at base bank. high yield interest rates. open within next week and get a free checking. irrelevant-agent: earn income working from home. no training is necessary. start making money right now. relevant but unpersuasive agent: get an overdraft protection. link a saving account with your checking one. we use a traditional advertisement format for the irrelevant and unpersuasive examples. a good example is a free-format text that includes a recommendation as well as its argumentative back up, an explanation why this product would solve a customer problem, as described in dialogue (ex. 4). negative examples rely on imperative form of verbs that is heavily used in conventional advertisement. to be a good recommendation, it needs to relate to the seed product and to its features and attributes that are the subjects of the conversation. in addition, discourse structure of the recommendation text matters (fig. 2 ). discourse tree representation (rst, @xcite for a recommendation allows to judge on its quality and can be constructed automatically @xcite . if rhetorical relations of explanation, cause, enablement are recognized in recommendation text @xcite then there is a higher chance that this recommendation is reasonable, persuasive and well argued. customer: there is a problem with feature f of product p agent: it can (or cannot be fixed) by doing (this and that) with f of p customer: no you still cannot fix problem of p … rjc-agent:product r will fix this problem with f of p since r's feature rf covers f to assure a recommendation makes sense to a user, it needs to be backed up by an argument. to find a textual recommendation that will be perceived by the user, this recommendation should form a well backed up claim where the utterances in the dialogue are premises. for argumentation support in rjc we employ a modified toulmin's model @xcite which contains five argument components, namely: claim, premise, backing, rebuttal, and refutation. in this model any arbitrary token span can be labeled with an argument component; the components do not overlap. all components are optional (they do not have to be present in the argument) except the claim, which is either explicit or implicit. if a token span is not labeled by any argument component, it is not considered as a part of the argument. relations from this model can be constructed automatically using extended discourse tree representation @xcite . in case of the pair of products p and rp, a recommendation for rp must be supported by the customers' expression of their needs and problems in p. ## dialogue management in rjc agent once a recommendation utterance is delivered, the user may choose to continue conversation with ad-agent. then the following algorithm is applied (algorithm 1). to build a conversational grammar for dialogue management, we introduce the notion of adjacencypair, sequences of two utterances that are adjacent (not separated by an insertion sequence), produced by different speakers and ordered as a first ("initiative") and a second ("response") part. both parts should also belong to a certain type, so that a particular initiative requires a certain type or range of types of the response. adjacency-pairs are question-answer, greeting-greeting, or offer-acceptance/decline. where there is a range of potential responses to an initiative (as with offer-acceptance/decline), a ranking operates over the options designating one response as preferred (in the sense of normal, more usual) and others ## system architecture high-level system architecture of rjc is shown in fig. 3 . the system tracks the dialogue and attempt to identify a moment where the customer is about to give up on the csa problem resolution, or is still unhappy after the problem is solved. this tracking is done based on emotional profile and sentiment profile @xcite . once such utterance is identified, rjc finds a noun phrase in it, and then identifies a product name together with its feature. entity extraction is done by stanford nlp augmented by the product-specific entity rules and product-specific lookup such as ebay product catalogue. product-related named entities could also be verified by consulting ebay product search api. then a search query from the formed product name and its feature is formed, and a search is launched. the search results form a list of candidates, which are filtered based on the proper argumentation and discourse coordination requirements. this filtering is implemented via argument mining and reasoning techniques. they verify that the recommendation as a claim is logically supported by the previous customer utterance and therefore this recommendation would be convincing for the customer. rhetorical agreement @xcite is verified based on coordination between the discourse trees of previous customer utterances and the discourse tree of the candidate recommendation text. 7 evaluation to get closer to the csa conversation setting, we selected relational strategies in customer service dataset that is collection of travel-related customer service data from four sources. the conversation logs of three commercial customer service ivas and the airline forums on tripadvisor.com. for a special case of conversations related to overall product opinion we employ the customer support on twitter dataset. it includes over 3 million tweets and replies from the biggest brands on twitter. the datasets to evaluate rjc are enumerated in the reddit discourse dataset @xcite is manually annotated with dialog-acts via crowd sourcing. the dialogue acts comprise of answer, question, humor, agreement, disagreement, appreciation, negative reaction, elaboration, and announcement. it contains conversations from around 9000 randomly sampled reddit threads with over 100000 comments and an average of 12 turns per thread. ## related work in the course of a customer support dialogue, recommendation and advertisement need to be very relevant to customer needs and should assist in problem resolution in the way obvious to this user. in a conventional conversational recommendation, the system first gets information from the user about his needs and preferences and recommends a product after that. to manage such a dialogue in an arbitrary domain, nontrivial dialogue management efforts are required @xcite @xcite @xcite . moreover, a user needs to be very patient and perform a routine activity of specifying his preferences. neither of these is required in rjc setting. argument mining techniques make it possible to capture the underlying motivations consumers express in reviews. @xcite describe how argument detection can occur on the textcoop platform. taking the dialogical perspective, @xcite built upon an argumentation framework proposed by @xcite which models arguments within a graph structure and provides a reasoning mechanism for resolving accepted arguments. a number of studies investigated persuasiveness in the sense that is applied to advertising. schlosser (2011) investigated persuasiveness of online reviews and concluded that presenting two sides is not always more helpful and can even be less persuasive than presenting one side. @xcite describe a computational model that attempts to integrate emotional and non-emotional persuasion. bernard et al. (2012) investigate children's perception of discourse connectives to link statements in arguments and found out that 4-years-old and adults are sensitive to the connectives. advertising in the course of dialogue is connected with dialogue marketing that is the generic term for all marketing activities in which media is used with the intention of establishing an interactive relationship with individuals. the aim is to initiate an individual, measurable response from the recipient @xcite . a relationship dialogue is a process of reasoning together in order for two or more parties to develop a common knowledge platform @xcite . relationship marketing knowledge platform enables a supplier to create additional value for its customers on top of the value of the goods which are exchanged in the relationship. there have been many works emphasizing the importance of interactivity in recommenders so that the user has more active role over the recommendations. it includes critique-based recommendations @xcite , constraint-based @xcite , dialogue, utility-based recommenders. however, these studies employ a prior modeling of the items' features, preventing the flexibility in adaptation to different recommendation domains. ## paper summary and conclusion we observe that it was necessary to track sentiments and the strength of emotion in the user-csa conversation. when sentiment is not too negative and emotion is not too strong it might be too early to induce a recommendation since there is a chance that the conflict is resolved among the humans. if the sentiment and emotions are too negative, it is time for a recommender to intervene. this way we achieve timeliness, less intrusiveness and overall relevance of rjc recommendation. the goal of our rjc dialogue manager is to "interrupt politely". we believe that in general, a sponsored post does not have to be necessarily irrelevant; a broader match with a catalog of sponsor products needs to be implemented so that every user can get a recommendation according to her specific interests and desires, expressed in communication with peers. also, the proposed algorithm would not deliver annoying repetitive recommendations as most advertisers and industrial recommender systems do. we summarize this paper by enumerating the observed features of rjc: 1) recommendation by joining a conversation turns out to have a high overall relevance and appropriateness to the needs of customers; 2) the accuracy range of 68-74% shows that at least 0.75 of recommendations should not cause user irritation and instead encourage a user to buy a recommended product; 3) in most cases the recommended products and services indeed address a customer problem raised in conversation; 4) explainable ai -compliant: it is clear why this product is needed; 5) this form of recommendation and advertisement is least intrusive as the rjc utterance can be ignored. one of the tasks of a future study is to evaluate an actual convergence rate of the rjc advertisement mode.