2015 Proceeding
- Sponsor:
- sigchi
This volume contains the papers presented at the ACM RecSys Challenge 2015 workshop held on September 16, 2015, in Vienna, Austria. The challenge offered participants the opportunity to work on a large-scale e-commerce dataset from a big retailer in Europe. Participants tackled the problem of predicting what items a user intends to purchase, if any, given a click sequence performed during an activity session on the e-commerce website. The challenge was launched on November 15, 2014, and ran for seven months, attracting 850 teams from 49 countries which submitted a total of 5,437 solutions. The winners were determined based on the final ranking of the scores at the end of the challenge. However, in order to receive the monetary prize, the participants were required to submit, and have accepted, a paper detailing the applied algorithms, and attend the challenge's workshop. There were 22 submissions, and each submission was reviewed by at least two program committee members. The following table contains a summary of the 12 accepted papers and the corresponding score and rank in the final leaderboard.
Proceeding Downloads
RecSys Challenge 2015: ensemble learning with categorical features
In this paper, we describe the winning approach for the RecSys Challenge 2015. Our key points are (1) two-stage classification, (2) massive usage of categorical features, (3) strong classifiers built by gradient boosting and (4) threshold optimization ...
E-Commerce Item Recommendation Based on Field-aware Factorization Machine
The RecSys 2015 contest [1] seeks the best solution to a top-N e-commerce item recommendation problem. This paper describes the team Random Walker's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the ...
Two-Stage Approach to Item Recommendation from User Sessions
We present our solution to the 2015 RecSys Challenge [1]. This challenge was based on a large scale dataset of over 9.2 million user-item click sessions from an online e-commerce retailer. The goal was to use this data to predict which items (if any) ...
Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization
- Róbert Pálovics,
- Péter Szalai,
- Levente Kocsis,
- Adrienn Szabó,
- Erzsébet Frigó,
- Júlia Pap,
- Zsófia K. Nyikes,
- András A. Benczúr
The RecSys Challenge 2015 task requested prediction for items purchased in online web shop sessions. We describe our method that reached fifth place on the leaderboard by constructing a large number of item, session, and session-item features and using ...
Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data
This paper describes some of the key properties of the proposed solution for the RecSys 2015 Challenge from the team Tøyvind thørrud. Three contributions will be highlighted: i) Feature extraction, ii) Classifier design, and iii) Decision rules to ...
Purchase Prediction and Item Suggestion based on HTTP sessions in absence of User Information
In this paper, the task is to determine whether an HTTP session buys an item, or not, and if so, which items will be purchased. An HTTP session is a series of item clicks. A session has type buy, if it buys at least one item, or non-buy otherwise. ...
An ensemble approach for multi-label classification of item click sequences
In this paper, we describe our approach to RecSys 2015 challenge problem. Given a dataset of item click sessions, the problem is to predict whether a session results in a purchase and which items are purchased if the answer is yes.
We define a simpler ...
Predicting User Purchase in E-commerce by Comprehensive Feature Engineering and Decision Boundary Focused Under-Sampling
The goal of RecSys Challenge 2015 [2] is: (1) to predict which user will end up with a purchase and if so, (2) to predict items that he/she will buy given click/purchase data provided by YOOCHOOSE. It is hard to achieve the goal of this Challenge ...
Linear and Non-Linear Models for Purchase Prediction
In this paper, we present our approach for the task of product purchase prediction. In the task, there are a collection of sequences of click events: click sessions. For some of the sessions, there are also buying events. The target of this task is to ...
In-House Solution for the RecSys Challenge 2015
RecSys Challenge 2015 is about predicting the items a user will buy in a given click session. We describe the in-house solution to the challenge as guided by the YOOCHOOSE team. The presented solution achieved 14th place in the challenge's final ...
Multi-Perspective Modeling for Click Event Prediction
We present our solutions to the RecSys Challenge 2015. We propose a multi-perspective modeling scheme for click event prediction, which involves techniques from sophisticated feature engineering for both click sessions and clicked items, classification ...
Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns
In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and ...
Index Terms
- Proceedings of the 2015 International ACM Recommender Systems Challenge
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| RecSys '19 | 189 | 36 | 19% |
| RecSys '18 | 181 | 32 | 18% |
| RecSys '17 | 125 | 26 | 21% |
| RecSys '16 | 159 | 29 | 18% |
| RecSys '15 | 131 | 28 | 21% |
| RecSys '15 Challenge | 21 | 12 | 57% |
| RecSys '14 | 234 | 35 | 15% |
| RecSys '13 | 136 | 32 | 24% |
| RecSys '12 | 119 | 24 | 20% |
| Overall | 1,295 | 254 | 20% |