Overview of Diversified Top-k Retrieval (DTkR)
Diversified Top-k Retrieval (DTkR) is a method for obtaining diversified top-k results in information retrieval and ranking tasks, aiming to obtain search results with different perspectives and diversity rather than simple Top-k results.
In general Top-k searches, the objective is simply to obtain the top k results with the highest scores, but similar results tend to rank high and lack diversity. DTkR, on the other hand, aims to make the search results more diverse and different, and allows information retrieval with a diversity that cannot be obtained with simple Top-k search results.
The advantages of DTkR include the following
Increased diversity: DTkR allows users to obtain not only similar results, but also results with different perspectives and diversity.
Covering a range of user interests: where users have a wide range of interests and needs, DTkR can provide a wide range of information.
Gives a holistic view of the information: it gives a holistic view of the search results, without being biased towards a single perspective or feature.
The specific steps of DTkR are as follows:
1. Define a diversity measure: DTkR defines a scale to measure the diversity between different search results. This measure may be, for example, similarity or distance. 2.
2. obtaining Top-k candidates: first, a normal Top-k search is performed to obtain the top-k candidates with the highest scores.
3. Selection method to maximise diversity: Among the candidates, a selection method that maximises diversity is used to determine the final top-k candidates. These selection methods include.
Greedy Diversification: this is a greedy method where the most diverse candidate is selected at each step. For example, the one with the least similarity to the elements selected in each step is selected.
Submodular Function Maximisation: also known as submodular function maximisation, this method is used to guarantee optimal diversity. Unlike the greedy method, it selects the optimum diversity by solving an optimisation problem.
Facility Location: This method selects candidates in such a way that the distance between them is maximised. Candidates are selected such that the distance is maximised.
4. obtaining the top k: based on the above methods, the final top k is obtained. This allows diverse search results to be obtained.
Algorithms related to Diversified Top-k Retrieval (DTkR).
Algorithms related to Diversified Top-k Retrieval (DTkR) include methods for selecting top-k cases while maximising diversity. They are described below.
1. Greedy Diversification: Greedy Diversification is a method for maximising diversity using the greedy method and works by the following steps:
1. as an initialisation, prepare an empty selection set.
2. at each step, among the remaining candidates, the one with the smallest similarity to the current selection set is selected and added to the selection set.
3. repeat the above steps k times.
This method greedily maximises diversity, as the most diverse candidates are selected at each step.
2. submodular Function Maximisation: submodular Function Maximisation is a method to guarantee optimal diversity, which is a function with the following properties.
The more elements are added, the smaller the added effect (subadditivity). In other words, ǫ( f(A \cup \{x\}) – f(A) \geq f(B \cup \{x\}) – f(B) \)).
This property is used to select the element that maximises diversity using the submodular function maximisation algorithm.
3. facility location: facility location is a method for selecting candidates so as to maximise the distance between them, and is based on the Facility Location Problem. The specific procedure is as follows:
1. consider each candidate as a facility and consider the distance from the facility to the customer (other candidates).
2. when selecting the top k candidates, select the candidate that maximises the distance to the other candidates.
3. maximising the distance yields a selection set with high diversity.
4. Sequential Greedy Selection: Sequential Greedy Selection is a type of greedy method in which each element is selected in turn and works by the following steps.
1. prepare an empty selection set as initialisation.
2. at each step, from among the remaining candidates, select the element with the largest increase in diversity when added to the selection set and add it to the selection set.
3. repeat the above steps k times.
This method maximises diversity by selecting the element with the largest increase in diversity at each step.
Application examples of Diversified Top-k Retrieval (DTkR)
The following are examples of applications of DTkR.
1. information retrieval: in information retrieval systems, DTkR provides search results from diverse perspectives. For example, it is useful when a user searches for a certain keyword and wishes to obtain information not only in terms of relevance but also from different fields and angles, thereby enabling a wide range of user interests and needs to be met.
2. product recommendations: in e-commerce sites and online stores, product recommendations are made using DTkR in order to attract users. This is done by suggesting products of different categories and types, as well as similarities, based on what the user has purchased or browsed in the past.
3. customising news feeds: in news apps and websites, DTkR is used to provide news articles that users might be interested in. By providing a balance of articles from different fields and perspectives, news feeds are customised to match user interests.
4. event and travel planning: in event and travel planning, DTkR is used to suggest sights and activities that might be of interest to users. By suggesting a good balance of spots with different categories, regions and characteristics, users’ travel plans are made more diverse.
5. delivery of online advertising: the DTkR is also useful in the delivery of online advertising. By providing users with different categories and types of advertisements, it increases the diversity of advertisements and attracts user interest.
6. validating and improving recommendation systems: DTkR is also used to evaluate and improve recommendation systems. It assesses whether the recommended items and content are diverse and adequately meet the needs of users, and provides information to improve the accuracy and satisfaction of the system.
Example implementation of Diversified Top-k Retrieval (DTkR).
As an example implementation of Diversified Top-k Retrieval (DTkR), Python code for the Greedy Diversification algorithm is shown. In this example, the top-k cases are selected based on some simple similarity matrix (similarity matrix) while maximising the diversity.
In the following example, a similarity matrix similarity_matrix is used where the similarity between each element is pre-computed.
import numpy as np
def greedy_diversification(similarity_matrix, k):
"""
Function to select the top k cases while maximising diversity using Greedy Diversification.
Args:
- similarity_matrix: Similarity matrix between elements. The similarity of each element must be pre-computed.
- k: Number of top k cases to be selected.
Returns:
- selected_indices: List of indices of selected elements.
"""
num_items = similarity_matrix.shape[0]
# Initialisation: empty selection set and list of indexes of selected elements.
selected_set = set()
selected_indices = []
# Repeat until the top k cases are selected.
for _ in range(k):
max_diversity = -np.inf
best_item = None
# Select the most diverse elements from the remaining candidates
for i in range(num_items):
if i not in selected_set:
diversity = 0
for j in selected_set:
diversity += similarity_matrix[i, j]
# Update on the most diverse elements.
if diversity > max_diversity:
max_diversity = diversity
best_item = i
# Add selected elements to the selection set.
selected_set.add(best_item)
selected_indices.append(best_item)
return selected_indicesIn this example, similarity_matrix is a similarity matrix between elements, where the similarity of each element must have been pre-computed. k is the number of top-k cases to be selected.
The function is based on the greedy method, which selects the most diverse elements at each step, and the top k cases are selected while maximising diversity by selecting the element with the minimum similarity to the elements selected at each step.
Challenges and measures for Diversified Top-k Retrieval (DTkR).
Diversified Top-k Retrieval (DTkR) has several challenges, and several measures exist to address these. They are discussed below.
Challenges:.
1. increased computational cost: DTkR incurs additional computational cost to select the top-k cases while maximising diversity. The problem is particularly acute when dealing with large data sets and high-dimensional similarity matrices.
2. difficulty in guaranteeing the optimal solution: in DTkR, it is generally difficult to guarantee the optimal solution. In particular, some methods, such as submodular function maximisation, may have difficulty in obtaining an optimal solution.
3. selection of appropriate diversity measures: the choice of measures of diversity is important, but it is difficult to choose the right measure for the task and the data.
4. sparsity of the data: if the similarity matrix is sparse (most of the elements are 0), it is difficult to maintain sufficient diversity.
Solution:
1. use efficient algorithms: use efficient algorithms or approximation methods to reduce computational costs. Some approaches include graph cuts and random sampling.
2. approximation of submodular functions: for methods for which the optimal solution is difficult, such as submodular function maximisation, approximation algorithms can be used to obtain results close to the optimal solution. These include combinations with the Greedy method and sampling methods.
3. selection of diversity measures: select the appropriate diversity measure depending on the task and data. For example, distance or similarity matrices may be used.
4. dealing with sparsity: devise a similarity calculation method to deal with sparsity issues. There are special methods that take sparsity into account, or methods that only consider partial similarity.
5. pre-training of rankings: to apply DTkR effectively, a ranking model may be trained in advance and used. Ranking models can have an evaluation function that takes diversity into account.
6. incorporating user feedback: user choice and feedback may be used to increase diversity and improve ranking. Adapt to user trends through online learning and real-time updates.
7. diversity weighting: weighting elements according to their diversity may improve performance while retaining appropriate diversity. Weighting ensures that there is not too much bias towards certain features or attributes.
Reference Information and Reference Books
For general machine learning algorithms including search algorithms, see “Algorithms and Data Structures” or “General Machine Learning and Data Analysis.
“Algorithms” and other reference books are also available.
1. articles
Diversifying Search Results
Jaime Carbonell and Jade Goldstein, SIGIR 1998
→ Proposed Maximal Marginal Relevance (MMR) and formulated the trade-off between similarity and diversity.
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
Explicit web search result diversification
Efficient Diversification of Web Search Results
Santo Fortunato et al.
→ Balancing the diversity of Top-k lists and efficient algorithms. 2.
2. reference books (theory and practice)
A. Standard books on information retrieval and ranking theory
Modern Information Retrieval (by Ricardo Baeza-Yates and Berthier Ribeiro-Neto)
→ A classic book on information retrieval. Essential for the basics of DTkR, including ranking, query processing, and evaluation metrics.
Introduction to Information Retrieval (by Manning, Raghavan, Schütze)
→ A Stanford textbook on information retrieval, covering MMR and diversity evaluation metrics (nDCG-IA, etc.).
Recommender Systems: An Introduction (by Dietmar Jannach et al.)
→ A. The treatment of diversity in recommender systems is also closely related to DTkR.
B. Connections with Applications and Current Research
Search Result Diversification (Synthesis Lectures on Information Concepts, Retrieval, and Services)
by Chirag Shah
→ This book focuses on specialized search result diversification (DTkR). Applications and algorithms are also covered.
Learning to Rank for Information Retrieval by Tie-Yan Liu
→ Integrates learning approaches and diversification in Top-k ranking.
コメント