KEYNOTE EXTENDED ABSTRACT

Towards Generative Search andRecommendation: A keynote at RecSys 2023
Tat-Seng Chua
National University of Singapore
Singaporedcscts@nus.edu.sg

Abstract
The emergence of large language models (LLM’s), especially ChatGPT, has for the firsttime make AI known to almost everyone and affected every facet of our society. The LLMs
have the potential to revolutionize the ways we seek and consume information. This has
stemmed the recent trends in both academia and industry to develop LLM-based generative
AI systems for various applications with enhanced capabilities. One such systems is thegenerative search and recommender system, which is capable of performing content retrieval,content repurposing, content creation and their integration to meet users’ information needs.
However, before such systems can be widely used and accepted, we need to address several
challenges. The primary challenge is the trust and safety in the generated content as we
expect the LLM’s to make mistakes with hallucination. This is because of the quality of data
being used for their training is often erroneous and biased. The other challenges in the searchand recommendation domain include: how to teach the system to be pro-active in anticipating
the needs of users and in directing the conversation towards a fruitful direction; as wellas the integration of retrieved and generated content. This keynote presented a generative
information seeking paradigm, and discuss key research towards a trustable generative system
for search and recommendation.
Date: 21 September 2023.

1

Introduction

The emergence of large language models (LLMs), such as the ChatGPT, has ushered in a new eraof AI. It has, for the first time, made AI known to everyone and affected almost every facet of oursociety. In particular, LLMs have exhibited strong language and common-sense capability withrich knowledge. They also possess the ability for instruction following, in-context learning, as well
as planning in the form of chain of thoughts. As a result, we can now freely converse with such
systems to express our intent in a fine-grained and multimodal manner, and we expect the system
to recommend existing items or generate new items as necessary, and present them in a concise
summarized form. This has stemmed the recent trend in both academia and industry to develop
LLM-based generative AI systems for various applications with enhanced capabilities. One such
example is the Search Co-Pilot introduced by many Internet Search Engines, such as the Microsoft
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Bing, to leverage LLM as supplement to search. The other example is the generative search andrecommender system, which can help to enrich content retrieval with content repurposing, content
creation, and the generation of search and recommendation rank list, in a seamless and coherent
manner. In addition, LLMs have been evolved into LMFMs (Large Multimodal Foundation Models) with the incorporation of multimodal contents, along with expanded applications in many
emerging multimodal domains.
However, before such systems can be widely used and accepted, we need to address several
limitations and challenges. One of the key limitations of LLMs is that although they are good at
knowledge, they are not the expert in most vertical domains. Moreover, they often make mistakes
along with the problem of hallucination, i.e. the tendency to make up answers that are fraudulenceor non-existence. This is because the vast amount of data used to train LLMs often contain errors
with biases. Also, although LLMs have superior 2D layout and reasoning capability, they are weakin 3D. They are also not good at logic and logical reasoning. As a result, LLMs are vulnerable
to attacks in retuning unsafe and privacy contents. In addition, LLMs are expensive to operate
especially online. Hence a lot of research is still needed before LLM-based models can be employedin many mission-critical applications that require reliable and trustable results.
Although there are many problems as listed above, it is often not an option for large corporations and government organizations not to embrace LLMs. The key is in deeply understanding
the limitations and challenges to come up with solutions to alleviate them in an effective and efficient manner. Given the resource constraints of many academia environments, we identify several
research directions that can be pursued academically; they are: (a) generative recommendation
systems; (b) LLM-powered AI Agents for emerging applications; (c) pro-active dialogue systems;and (d) trust and safety in LLM-based systems. Of course, there are also many more fruitfulresearch directions that could be explored, including, the extraction of basic common capabilityof LLMs, the injection of personality and diversity into LLMs, the evaluation and audit of LLMs,
and the fine-grained visual understanding, etc. In the following, we will elaborate our research on
the four research focuses.

2

Generative Recommender Systems

The boom of generative models has paved the way for significant advances in recommender systems. Recently, there have been emerging innovative ideas on integrating generative models, especially LLMs, into recommender systems to reshape the traditional recommender paradigm andshed light on generative recommender systems. As shown in Figure 1, we examine the development
prospects of generative recommender systems from three aspects: 1) enhancing recommender algorithms [Bao et al., 2023b], 2) changing user-system interaction model [Liao et al., 2023a], and3) generating personalized content [Wang et al., 2023c].
Recent research on using LLMs to enhance recommender algorithms falls into two groups:
LLMs-enhanced and LLMs-based recommender systems [Wu et al., 2023]. LLMs-enhanced recommender systems primarily leverage the rich world knowledge, language understanding andgeneration capabilities of LLMs to enhance existing recommendation systems [Bao et al., 2023a;
Wu et al., 2023]. For example, prior studies utilize LLMs to enrich the semantic representationsof users and items in existing recommendation systems [Xi et al., 2023a]. In contrast, LLM-based
recommender systems directly adopt LLMs to rank items for recommendations [Wu et al., 2023].
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2

User
instructions

3 AIGC

Human
uploader1 Generative retrieval/ranking

3 AIGC

Figure 1. Illustration of the three aspects of incorporating generative models into the traditional
recommender systems.

Since the LLM pretraining phase does not involve recommendation tasks, directly applying LLMs
for recommendations often yields limited results [Bao et al., 2023b]. It is thus crucial to utilize
recommendation data for instruction tuning on LLMs. For instance, TALLRec tunes LLMs forrecommendation using few-shot recommendation samples, finding that a small number of samples can significantly improve Llama’s recommendation performance [Bao et al., 2023b]. BIGRec
achieves comparable or even better recommendation results than the traditional recommendation models through instruction tuning and generation grounding [Bao et al., 2023a]. TransRec
discovers that using multi-facet item identifiers for instruction tuning can better bridge the recommendation data and the language space of LLMs [Lin et al., 2023b]. In addition to LLMs, previous
work has also explored other generative models like diffusion models for recommendation [Wanget al., 2023d; Li et al., 2023c], validating the promising direction of generative item rankings.
Generative models also have immense potential to revolutionize the existing user-system interaction paradigm [Liao et al., 2023a]. Traditional recommender systems often rely on various
forms of implicit user feedback such as the clicks and likes to model user preference [Covington
et al., 2016] which may not quickly capture changes in user preference or understand users’ explicit
information needs [Wang et al., 2023c]. LLMs, due to their strong language understanding, reasoning and generation capabilities, can significantly enhance existing conversational recommendersystems, revolutionize the previous user-system interaction paradigm, and better capture users’
diverse information needs.
Generative models possess powerful content generation capabilities to complement existing
items and meet users’ personalized needs [Wang et al., 2023c]. Existing recommender systems often rank and recommend pre-generated items from the item corpus. However, the items within the
corpus may not always fulfill users’ specific information needs. For instance, a user might desire
clothing with a particular pattern. In such cases, generative models can be incorporated to produce AI-generated content (AIGC), i.e., repurposing existing items or creating new items [Wanget al., 2023c]. As such, in the recommendation ecosystem, it is likely to envision AIGC gradually
supplementing human-generated content.
Looking ahead to the future of generative recommender systems, we expect to see generativemodels gradually playing an important role in multiple aspects of the recommendation ecosystem.
This includes, but not limited to, improving item retrieval and ranking, diversifying user-system
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interactions, and enriching content generation. Additionally, there will be increasing efforts toconsider the trustworthiness of generative recommender systems from various aspects such asbias, fairness, and safety [Wang et al., 2023c; Zhang et al., 2023b].

3

LLM-powered AI Agents for Recommendations

Autonomous AI agents have long been regarded as stepping stones towards artificial general
intelligence (AGI), with capabilities for self-guided task execution. Recent advances in Large
Language Models (LLMs) have demonstrated a closer approximation to human-level intelligencein self-directed planning and instructions [OpenAI, 2023]. This advancement has spurred a growing
trend in integrating LLMs as central components in developing autonomous AI agents [Qin et al.,2023; Xi et al., 2023b]. In the domain of recommender systems - vital for navigating today’s vast
information landscape - LLM-powered AI agents exhibit superior performance in autonomousinteraction and in discerning user preference [Lin et al., 2023a]. Such impressive capability canbe harnessed to simulate authentic human behavior within recommender systems, reflecting bothindividual and population levels patterns [Yang et al., 2023].
Simulating user behavior in recommender systems is a complex endeavor that requires a deep
understanding of human preference and behavior patterns [Chen et al., 2023a; Zhang et al., 2023c].
To bridge this gap, the design of agent modules that are tailored for recommendation context
is crucial [Wang et al., 2023b; Zhang et al., 2023a]. Figure 2 depicts a framework for LLMpowered agents in recommendations; in which the profile module encapsulates personalized social
traits and the user’s historical preferences, the memory module records past interaction behaviors
and emotional memories, and the action module empowers agents to interact directly with therecommendation environment.
Real Data

User Profile

Movie Profile
Quality

Popularity

Activity

Page-by-Page RecommendationMF...
MultiVAE

Exit

Diversity

Individual Preferences

Movie Summary

Recommender
System Next Page

Conformity

Spider-Man: Into the Spider-Verse(2018)
Quality: 4.19 Popularity: 174 views
Summary: Miles Morales gains
superpowers after being bitten bya spider and becomes the unique
Spider-Man.
Iron Man (2008)
Quality: 3.82 Popularity: 261 views
Summary: Billionaire engineer Tony
Stark creates a unique weaponizedsuit of armor to fight evil.

ProfileMemoryRetrieval

MemoryLLM
AgentActionMovies Selection
Movies Rating
Satisfaction Generation
Next / Exit

MemoryUpdateLigntGCN

Figure 2. An illustrative example of LLM-powered Agent’s architecture to simulate user behavior inrecommender systems, where Agent’s module designs are highlighted in red [Zhang et al., 2023a].

The use of LLM-empowered agents to emulate human behavior has yielded encouraging results but also exposed inherent limitations. These explorations have revealed that: (1) agentsACM SIGIR Forum

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can preserve personalized attributes and distinct social traits embedded within the user profiles,
acting in accordance with the delineated user personas; (2) leveraging the data generated fromsimulations to retrain recommenders can boost their performance; and (3) certain challenges in
the recommendation domain, previously unresolved due to data scarcity, are now feasible to be
explored. However, several limitations remain, notably, (1) current attempt at AI Agents does
not adequately mirror the diverse scenarios and multimodal nature of recommender systems, compelling demand for agents with advanced multi-modal comprehension [Huang et al., 2023b]; and(2) purely prompting-based Agents fall short in effectively and accurately simulating human behavior, thereby making it essential to fine-tune these agents to better reflect the user preferences[Zhang et al., 2023a; Li et al., 2023b].

4

LLM-based Proactive Conversational Systems

LLM-based conversational systems have demonstrated exceptional proficiency in context understanding and response generation in various dialogue problems. However, as LLMs are trained
to passively follow users’ instructions, conversational agents built upon them typically prioritize
accommodating users’ intention. Therefore, LLM-powered conversational agents often face challenges in handling proactive dialogue problems [Deng et al., 2023c; Liao et al., 2023b] that require
the conversational agent to strategically take the initiative to steer the conversation towards ananticipated goal [Deng et al., 2023b]. In recent years, we have witnessed many advanced designs
to make the conversational agents proactive in meeting a wide range of challenging dialogue problems, such as the clarification in information-seeking dialogues [Deng et al., 2022], target-guided
dialogues [Lei et al., 2022], and non-collaborative dialogues [Zhan et al., 2022], etc.
Recent works investigate prompt-based methods to trigger the proactivity of LLMs [Chenet al., 2023b; Deng et al., 2023d]. For example, Chen et al. [2023b] suggest using LLMs togenerate controllable responses based on pre-defined dialogue strategies, though these strategies
are not always available in dynamic real-world scenarios. To this end, Deng et al. [2023d] evaluate
the capability of LLMs in making strategic decisions for different types of proactive dialogues
from the perspective of prompting. The Proactive Chain-of-Thought (ProCoT) prompting scheme(Figure 3) is proposed to plan the next dialogue action with chain-of-thoughts, i.e., intermediate
steps of reasoning and planning the next action to take in order to reach the conversational goal.
The empirical analysis reveals that while ProCoT is successful in certain dialogue types, there
are still many limitations: (1) it falls short of handling domain-specific dialogue applications,such as Finance; (2) it tends to make aggressive topic transitions due to the strong capability ofcontrollable text generation; and (3) it fails to make strategic decisions to lead the conversationin non-collaborative dialogues.
The future holds great promise for LLM-based proactive conversational systems, as researchcontinues to bridge the gap between passive response generation and proactive dialogue planning[Deng et al., 2023e]. This evolution will spark in a new era of human-computer interactions,marked by more intuitive, strategic and dynamic conversations tailored to individual user needs
and broader applications.

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Figure 3. Examples of three kinds of prompting schemes for proactive dialogues [Deng et al., 2023d].

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5

Trust Evaluation of LLM

Despite the impressive capabilities of LLM across a wide spectrum of language tasks, LLM is
not infallible and is susceptible to issues related to trust. LLM has been observed to produce
hallucinated or erroneous content, such as the inclusion of fabricated information or flawed reasoning [Zhang et al., 2023d; Wang et al., 2022]. Additionally, LLM is vulnerable to adversarial
and backdoor attacks [Wang et al., 2023a]. Hence, it is important to identify and address trustrelated problems in LLM-generated content to ensure its safe and dependable use. Approaches
for detecting trust issues in LLM-generated content can be categorized into two main types by
the source of the verification process: 1) fact-checking or external verification, and 2) consistency
checking or self evaluation. An overview of these approaches is summarized in Figure 4.
The external verification entails the validation of generated content with reliable knowledge
sources, systems, and trusted websites. For instance, the utilization of the reputable knowledge
engine Wolfram Alpha1 in conjunction with ChatGPT serves as a mean to accessing reliable information. Additionally, Gao et al. [2023a] and Zhao et al. [2023] leverage trusted websites foraddressing verification questions and performing trustworthy edits on the LLM-generated content. Furthermore, when dealing with numerical questions, Gao et al. [2023b] advocate shifting
the numerical calculation to a dependable Python interpreter instead of relying on LLM. Nevertheless, the external verification approach has its limitations, as certain trusted sources may
only be effective for specific categories of generated content and they may not contain up-to-date
information.
The self evaluation aims to explore the LLM’s inherent capability to express confidence inits generation, providing a more general solution for all LLM outputs. Researchers employ selfconsistency-based methods to obtain calibrated confidence scores [Kuhn et al., 2022; Kadavath
et al., 2022; Tian et al., 2023], which is adopted to enhancing accuracy in domains like molecular
reactions [Shi et al., 2023]. Moreover, self evaluation is also claimed to be accomplished by multiaspects verification, e.g., multi-agent debate [Du et al., 2023] or LLM critique [Cohen et al., 2023].
However, the effectiveness of such methods is questionable, since the correctness and confidenceof these LLM outputs remain unclear [Huang et al., 2023a]. Additionally, self evaluation canbe achieved through counterfactual verification, involving asking counterfactual questions andexamining answer consistency or answer confidence.
1

https://www.wolframalpha.com/.
Reliable Knowledge Sources

External Verification

Trusted Web Sites
External Tools

Trust Evaluation

Self-Consistency Checking

Self Evaluation

Multi-Aspects Verification
Counterfactual Verification

Figure 4. A summary for different trust evaluation approaches on LLM.

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In summary, the trust evaluation in LLM generation will continue to be a thriving area of
research, as the pursuit of dependable generated content from LLMs remains a long-term objective
for researchers and practitioners across diverse fields.

6

Safety Evaluation of LLM

LLMs play a crucial role in information dissemination and communication, making safety evaluation on LLM-generated content of utmost importance. The safety evaluation aims to prevent thegeneration of harmful content, including but not limited to biased, unfaithful, and discriminatory
responses [Sun et al., 2023, 2021; Zhang et al., 2023e]. In this research line, one crucial branch
is to design diverse attacks to assess LLM safety [Deng et al., 2023a]. By designing transparent
and controllable attack methods, we can examine the safety of LLM-generated content and also
provide various attack instances to help align LLMs with safety objectives.
As shown in Figure 5, we introduce two attack paradigms for LLM safety evaluation. The
first is using red teaming attacks to poison prompts, aiming to induce LLMs to generate harmful
responses [Perez et al., 2022]. By evaluating the harmfulness of responses to diverse poisoned
prompts, we can measure LLM safety from diverse aspects such as faithfulness, discrimination, andviruses. For example, previous research has explored using human-generated poisoned prompts toinstruct LLMs through in-context learning to generate more poisoned prompts for attacks [Denget al., 2023a]. To poison prompts, the popular strategies involve role play, payload splitting, andreverse prompts [Kang et al., 2023; Li et al., 2023a; Perez and Ribeiro, 2023]. The second paradigm
is to poison external data to attack LLMs. It is common to retrieve external data (e.g., the latest
events or documents) to assist LLMs in generating responses [Greshake et al., 2023; Pan et al.,2023b]. We can inject harmful information into external data to induce the retrieval-based LLMs
to generate harmful content. For instance, prior work has considered injecting documents withmisinformation to induce LLMs to generate incorrect answers [Pan et al., 2023a].

Database<2> Poison
external data

<1> Poison
prompts

PromptSafety evaluation(e.g., latest
documents)

retrieve

returnTampered doc

Generated
Content

LLMs-based
applications

Harmfulness
Faithfulness
Misinformation
Discrimination
Bias
etc.

Figure 5. Illustration of safety evaluation by poisoning prompts and external data.

LLM safety evaluation requires extensive future endeavors from various aspects, including
expanding attack targets, refining attack methods, and enhancing evaluation tools to inspect
generated content from multiple perspectives. Besides, how to better utilize attack instances to

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enhance LLM safety is also an important concern. Moreover, moving beyond LLM safety evaluation, there is a broad landscape in LLM evaluation, including performance measures (e.g., speed,
accuracy, and diversity), content measures (e.g., commonsense knowledge, domain expertise, andweaknesses), trust measures (e.g., authenticity, bias and fairness, privacy, safety, and legal compliance). It signifies promising avenues for future research.

7

Summary

The popularity of ChatGPT, or more generally LLM, has made AI known to almost everyone
and affected every facet of our society. As discussed, LLM possesses strong natural language,common sense and reasoning capabilities, and is capable of comprehending a huge amount of data
corpus within any organization, correlating them with relevant external data via retrieval, andproviding a summary or answering almost any question with some reasoning capabilities. Becauseof this, it has the potential to be used in many domains, ranging from E-Commerce, Finance,
Medical, Media and Education, etc. However, it has many shortcomings, including the problemof trust, safety and privacy, and is also costly to operate especially for online applications. Henceour research must not focus only on its strengths in applications like generative research andrecommendation, and proactive dialogs, but must address its weaknesses on trust and safety too.
In line with this, we have discussed four research directions that we can fruitfully carry out andcontribute in the academic environment. In addition, there are many more challenges that we cando and contribute that are not covered in this keynote.
In every decade, there are some technological innovations, such as the Internet, PC, Deep-Blue,
Alpha-Go, and now LLM, that have disrupted some aspects of our work and life. And each time
there were doomsday predictions that the machines are going to take over the world and rule overhumans. However, as we observed over the decades, and in this case, the LLM-based AI systems,
the machines are not good at handling complex tasks that require human-level intelligence andcommon sense. Thus, I see a future where the machines do the mundane jobs that humans hate todo, and leaving the more interesting higher-level jobs that require natural reasoning and creativity
to humans. Of course, in the future, the AI systems will become more and more intelligent, andwe should have the common sense of not to allow machines to make critical decisions. In summary,the crises that the machines are taking over the world are happening over and over, but we always
have the common sense to weather through them with better outcomes for our society.

Acknowledgments
I would like to thank An Zhang, Yang Deng, Moxin Li, and Wenjie Wang for their contribution
to this keynote and paper. This work is supported by NExT++ Research Center.

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