6 research outputs found
MALA: Cross-Domain Dialogue Generation with Action Learning
Response generation for task-oriented dialogues involves two basic
components: dialogue planning and surface realization. These two components,
however, have a discrepancy in their objectives, i.e., task completion and
language quality. To deal with such discrepancy, conditioned response
generation has been introduced where the generation process is factorized into
action decision and language generation via explicit action representations. To
obtain action representations, recent studies learn latent actions in an
unsupervised manner based on the utterance lexical similarity. Such an action
learning approach is prone to diversities of language surfaces, which may
impinge task completion and language quality. To address this issue, we propose
multi-stage adaptive latent action learning (MALA) that learns semantic latent
actions by distinguishing the effects of utterances on dialogue progress. We
model the utterance effect using the transition of dialogue states caused by
the utterance and develop a semantic similarity measurement that estimates
whether utterances have similar effects. For learning semantic actions on
domains without dialogue states, MsALA extends the semantic similarity
measurement across domains progressively, i.e., from aligning shared actions to
learning domain-specific actions. Experiments using multi-domain datasets, SMD
and MultiWOZ, show that our proposed model achieves consistent improvements
over the baselines models in terms of both task completion and language
quality.Comment: 9 pages, 3 figure
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering,
researchers usually make use of side information, such as social networks or
item attributes, to improve recommendation performance. This paper considers
the knowledge graph as the source of side information. To address the
limitations of existing embedding-based and path-based methods for
knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end
framework that naturally incorporates the knowledge graph into recommender
systems. Similar to actual ripples propagating on the surface of water, Ripple
Network stimulates the propagation of user preferences over the set of
knowledge entities by automatically and iteratively extending a user's
potential interests along links in the knowledge graph. The multiple "ripples"
activated by a user's historically clicked items are thus superposed to form
the preference distribution of the user with respect to a candidate item, which
could be used for predicting the final clicking probability. Through extensive
experiments on real-world datasets, we demonstrate that Ripple Network achieves
substantial gains in a variety of scenarios, including movie, book and news
recommendation, over several state-of-the-art baselines.Comment: CIKM 201