710 research outputs found
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Personalized Memory Transfer for Conversational Recommendation Systems
Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach
Ontology-based user modeling in an augmented audio reality system for museums
Ubiquitous computing is a challenging area that allows us to further our understanding and techniques of context-aware and adaptive systems. Among the challenges is the general problem of capturing the larger context in interaction from the perspective of user modeling and human–computer interaction (HCI). The imperative to address this issue is great considering the emergence of ubiquitous and mobile computing environments. This paper provides an account of our addressing the specific problem of supporting functionality as well as the experience design issues related to museum visits through user modeling in combination with an audio augmented reality and tangible user interface system. This paper details our deployment and evaluation of ec(h)o – an augmented audio reality system for museums. We explore the possibility of supporting a context-aware adaptive system by linking environment, interaction object and users at an abstract semantic level instead of at the content level. From the user modeling perspective ec(h)o is a knowledge based recommender system. In this paper we present our findings from user testing and how our approach works well with an audio and tangible user interface within a ubiquitous computing system. We conclude by showing where further research is needed
Pepper4Museum: Towards a Human-like Museum Guide
With the recent advances in technology, new ways to engage visitors in a museum have been proposed. Relevant examples range from the simple use of mobile apps and interactive displays to virtual and augmented reality settings. Recently social robots have been used as a solution to engage visitors in museum tours, due to their ability to interact with humans naturally and familiarly. In this paper, we present our preliminary work on the use of a social robot, Pepper in this case, as an innovative approach to engaging people during museum visiting tours. To this aim, we endowed Pepper with a vision module that allows it to perceive the visitor and the artwork he is looking at, as well as estimating his age and gender. These data are used to provide the visitor with recommendations about artworks the user might like to see during the visit. We tested the proposed approach in our research lab and preliminary experiments show its feasibility
Explaining recommendations in an interactive hybrid social recommender
Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to users. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work, the intelligent user interface has been investigated as an approach to increase transparency and improve the user experience. In this paper, we attempt to further promote the transparency of recommendations by augmenting an interactive hybrid recommender interface with several types of explanations. We evaluate user behavior patterns and subjective feedback by a within-subject study (N=33). Results from the evaluation show the effectiveness of the proposed explanation models. The result of post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of perceived controllability
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
Conversational recommender systems (CRSs) aim to provide recommendation
services via natural language conversations. Although a number of approaches
have been proposed for developing capable CRSs, they typically rely on
sufficient training data for training. Since it is difficult to annotate
recommendation-oriented dialogue datasets, existing CRS approaches often suffer
from the issue of insufficient training due to the scarcity of training data.
To address this issue, in this paper, we propose a CounterFactual data
simulation approach for CRS, named CFCRS, to alleviate the issue of data
scarcity in CRSs. Our approach is developed based on the framework of
counterfactual data augmentation, which gradually incorporates the rewriting to
the user preference from a real dialogue without interfering with the entire
conversation flow. To develop our approach, we characterize user preference and
organize the conversation flow by the entities involved in the dialogue, and
design a multi-stage recommendation dialogue simulator based on a conversation
flow language model. Under the guidance of the learned user preference and
dialogue schema, the flow language model can produce reasonable, coherent
conversation flows, which can be further realized into complete dialogues.
Based on the simulator, we perform the intervention at the representations of
the interacted entities of target users, and design an adversarial training
method with a curriculum schedule that can gradually optimize the data
augmentation strategy. Extensive experiments show that our approach can
consistently boost the performance of several competitive CRSs, and outperform
other data augmentation methods, especially when the training data is limited.
Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
Recommender models excel at providing domain-specific item recommendations by
leveraging extensive user behavior data. Despite their ability to act as
lightweight domain experts, they struggle to perform versatile tasks such as
providing explanations and engaging in conversations. On the other hand, large
language models (LLMs) represent a significant step towards artificial general
intelligence, showcasing remarkable capabilities in instruction comprehension,
commonsense reasoning, and human interaction. However, LLMs lack the knowledge
of domain-specific item catalogs and behavioral patterns, particularly in areas
that diverge from general world knowledge, such as online e-commerce.
Finetuning LLMs for each domain is neither economic nor efficient.
In this paper, we bridge the gap between recommender models and LLMs,
combining their respective strengths to create a versatile and interactive
recommender system. We introduce an efficient framework called InteRecAgent,
which employs LLMs as the brain and recommender models as tools. We first
outline a minimal set of essential tools required to transform LLMs into
InteRecAgent. We then propose an efficient workflow within InteRecAgent for
task execution, incorporating key components such as a memory bus, dynamic
demonstration-augmented task planning, and reflection. InteRecAgent enables
traditional recommender systems, such as those ID-based matrix factorization
models, to become interactive systems with a natural language interface through
the integration of LLMs. Experimental results on several public datasets show
that InteRecAgent achieves satisfying performance as a conversational
recommender system, outperforming general-purpose LLMs.Comment: 16 pages, 15 figures, 4 table
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