2,017 research outputs found
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
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
When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing
Carpooling, or sharing a ride with other passengers, holds immense potential
for urban transportation. Ridesharing platforms enable such sharing of rides
using real-time data. Finding ride matches in real-time at urban scale is a
difficult combinatorial optimization task and mostly heuristic approaches are
applied. In this work, we mathematically model the problem as that of finding
near-neighbors and devise a novel efficient spatio-temporal search algorithm
based on the theory of locality sensitive hashing for Maximum Inner Product
Search (MIPS). The proposed algorithm can find near-optimal potential
matches for every ride from a pool of rides in time and space for a small . Our
algorithm can be extended in several useful and interesting ways increasing its
practical appeal. Experiments with large NY yellow taxi trip datasets show that
our algorithm consistently outperforms state-of-the-art heuristic methods
thereby proving its practical applicability
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