Information retrieval

This group of approaches includes multiple implementations of information retrieval models, initially devised for searching text documents in massive information spaces. All the algorithms included here are collaborative filtering approaches (they do not use any feature information, just the connections in the network). Details about how these models were adapted and their formulations for contact recommendation can be found in the following references:

References:

1. Sanz-Cruzado, P. Castells, C. Macdonald, I. Ounis. Effective Contact Recommendation in Social Networks by Adaptation of Information Retrieval Models. Information Processing & Management 57(5) (2020).
1. Sanz-Cruzado, C. Macdonald, I. Ounis, P. Castells. Axiomatic Analysis of Contact Recommendation Methods in Social Networks: an IR perspective. 42th European Conference on Information Retrieval (ECIR 2020), 175-90 (2020).

The IR models included in this framework are:

Binary independent retrieval
BM25
DFRee
DFReeKLIM
DLH
DPH
Extreme BM25
Pivoted normalization vector space model
PL2
Query likelihood
Vector space model

Binary independent retrieval

Adaptation of the binary independent retrieval (BIR) model.

Reference: K. Sparck Jones, S. Walker, S.E. Robertson. A Probabilistic Model of Information Retrieval: Development and Comparative Experiments. Information Processing and Management 36, 779-808 (part 1), 809-840 (part 2) (2000).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))

Configuration file

BIR:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]

BM25

Adaptation of the BM25 probabilistic information retrieval model.

References:

1. Sparck Jones, S. Walker, S.E. Robertson. A Probabilistic Model of Information Retrieval: Development and Comparative Experiments. Information Processing and Management 36, 779-808 (part 1), 809-840 (part 2) (2000).
S.E. Robertson, H. Zaragoza. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval 3, 333–389 (2009).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
dlSel: the neighborhood selection for computing the document length.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
b: parameter for tuning the effect of the neighborhood size. It takes values between 0 and 1.
k: parameter for tuning the effect of the term frequency in the model. It takes positive values.
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

BM25:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  dlSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  b:
    type: double
    range:
      - start: 0.1
        end: 0.99
        step: 0.1
  k:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

DFRee

Adaptation of a parameter-free divergence from randomness model using the average of tw information measures.

Reference: G. Amati, G. Amodeo, M. Bianchi, G. Marcone, F.U. Bordoni, C. Gaibisso, G. Gambosi, A. Celi, C.D. Nicola, M. Flammini. FUB, IASI-CNR, UNIVAQ at TREC 2011 Microblog Track. 20th Text REtrieval Confer-ence (TREC 2011) (2011).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

DFRee:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  (weighted:
    type: boolean
    values: [true,false])

DFReeKLIM

Adaptation of a parameter-free divergence from randomness model using the product of two Kullback-Leibler information measures.

Reference: G. Amati, G. Amodeo, M. Bianchi, G. Marcone, F.U. Bordoni, C. Gaibisso, G. Gambosi, A. Celi, C.D. Nicola, M. Flammini. FUB, IASI-CNR, UNIVAQ at TREC 2011 Microblog Track. 20th Text REtrieval Confer-ence (TREC 2011) (2011).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

DFReeKLIM:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  (weighted:
    type: boolean
    values: [true,false])

DLH

Adaptation of a parameter-free divergence from randomness model which considers a hypergeometric distribution as a divergence measure, and Laplace normalization.

Reference: G. Amati. Frequentist and Bayesian Approach to Information Retrieval. In: Proceedings of the 28th European Conference on Information Retrieval (ECIR 2006), 13–24 (2006).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

DLH:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  (weighted:
    type: boolean
    values: [true,false])

DPH

Adaptation of a parameter-free divergence from randomness model which considers a hypergeometric distribution as a divergence measure, and Laplace normalization.

Reference: G. Amati, E. Ambrosi, M. Bianchi, C. Gaibisso, G. Gambosi: FUB, IASI-CNR and University of Tor Vergata at TREC 2007 Blog Track. 16th Text REtrieval Conference (TREC 2007) (2007)

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

DPH:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  (weighted:
    type: boolean
    values: [true,false])

Extreme BM25

A version of the BM25 algorithm, when parameter k tends to infinity.

References:

1. Sanz-Cruzado, P. Castells, C. Macdonald, I. Ounis. Effective Contact Recommendation in Social Networks by Adaptation of Information Retrieval Models. Information Processing & Management 57(5) (2020).
1. Sanz-Cruzado, C. Macdonald, I. Ounis, P. Castells. Axiomatic Analysis of Contact Recommendation Methods in Social Networks: an IR perspective. 42th European Conference on Information Retrieval (ECIR 2020), 175-90 (2020).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
dlSel: the neighborhood selection for computing the document length.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
b: parameter for tuning the effect of the neighborhood size. It takes values between 0 and 1.
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

EBM25:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  dlSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  b:
    type: double
    range:
      - start: 0.1
        end: 0.99
        step: 0.1
  k:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

Pivoted normalization vector space model

Adaptation of the vector space model information retrieval model with pivoted normalization.

Reference: A. Singhal, J. Choi, D. Hindle, D.D. Lewis, F.C.N. Pereira: AT and T at TREC-7. 7th Text Retrieval Conference (TREC 1998), 186-198 (1998)

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
s: parameter for tuning the importance of the candidate user length.
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

Pivoted normalization VSM:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  s:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

PL2

Adaptation of a divergence from randomness model, where the distribution of terms in the document and the collection is measured using a Poisson distribution, a Laplace aftereffect estimation is used as a first normalization, and, term frequency is normalized using Normalisation 2.

References:

1. Amati, C.J. Van Rijsbergen. Probabilistic Models of Information Retrieval Based on Measuring the Divergence from Randomness. ACM Transactions on Information Systems 20(4), 357–389 (2002).
1. Amati. Probability Information Models for Retrieval based on Divergence from Randomness. Ph.D. thesis. University of Glasgow. (2003).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
c: parameter for tuning the importance of the candidate user length.
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

PL2:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  c:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

Query likelihood

Adaptation of a language model algorithm known as query likelihood. We differentiate three variants, depending on the applied smoothing:

Dirichlet smoothing (QLD)
Jelinek-Mercer smoothing (QLJM)
Laplace additive smoothing (QLL)

References: J.M. Ponte, W.B. Croft. A language modeling approach to information retrieval. 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), 275-281 (1998)

Parameters

The general parameters are the following:

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Then, each variant has its own parameters. For the QLD version:

mu: parameter controlling the trade-off between the regularization term and the original term. It takes values greater than 0.

for the QLJM version:

lambda: parameter controlling the trade-off between the regularization term and the original term. It takes values between 0 and 1.

and, for the QLL version:

phi: parameter controlling the trade-off between the regularization term and the original term. It takes values greater than 0.

Configuration file

The configuration file for the query likelihood algorithm with Dirichlet smoothing is:

QLD:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  mu:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

The configuration file for the query likelihood model with Jelinek-Mercer smoothing is:

QLJM:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  lambda:
    type: double
    range:
      - start: 0.1
        end: 0.99
        step: 0.1
  (weighted:
    type: boolean
    values: [true,false])

And, finally, for the query likelihood model with Laplace smoothing:

QLL:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  phi:
    type: double
    values: [0.01,0.1,1,10,100]
  (weighted:
    type: boolean
    values: [true,false])

Vector space model

Adaptation of the original vector space model in information retrieval.

Reference: G. Salton, A. Wong, C.S. Yang. A vector space for automatic indexing. Communications of the ACM 18(11), 613-620 (1975).

Parameters

uSel: the neighborhood selection for the target user.
- IN: it considers the incoming neighborhood of the target user.
- OUT: it considers the outgoing neighborhood of the target user.
- UND: it considers the all the possible neighbors of the target users (\(\Gamma_{out}(u) \cup \Gamma_{in}(u)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the target user (\(\Gamma_{out}(u) \cap \Gamma_{in}(u)\))
vSel: the neighborhood selection for the candidate user.
- IN: it considers the incoming neighborhood of the candidate user.
- OUT: it considers the outgoing neighborhood of the candidate user.
- UND: it considers the all the possible neighbors of the candidate users (\(\Gamma_{out}(v) \cup \Gamma_{in}(v)\))
- MUTUAL: it considers as neighbors those who share a reciprocal link with the candidate user (\(\Gamma_{out}(v) \cap \Gamma_{in}(v)\))
weighted: (OPTIONAL) true to use the weights of the edges, false to consider them binary.

Configuration file

VSM:
  uSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  vSel:
    type: orientation
    values: [IN,OUT,UND,MUTUAL]
  (weighted:
    type: boolean
    values: [true,false])